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This article lists words and their definitions as used in my research.


  1. Dissertation: Compiled research at the doctoral level.
  2. Thesis: Original research at the masters level.
  3. Digitalmass: The Digital Life in a system as measured by the memory it occupies.
  4. Academic/Scientific Article: Usually called a paper but will now be called an article because few actually see paper anymore.
  5. Dissertation vs. Thesis[1]
    1. UF uses dissertation for the doctoral program, so adopt that usage. In Britain, the usage is the opposite.
  6. Entrenched Idiocy: When people would rather believe the lie than admit they were lied to, fooled, or gullible.
  7. Nailing Smoke to the Wall
  8. Herding Spiders

Artificial Life

  1. Artificial Life[2] A terminology coined by C G Langton to denote the "... study of simple computer generated hypothetical life forms, i.e. life-as-it-could-be." Artificial life and evolutionary computation have a close relationship because evolutionary algorithms are often used in artificial life research to breed the survival strategies of individuals in a population of artificial life forms.
  2. Artificial Life (Soft) - Software that behaves like biological life. See Artificial Life (Soft) Books.
  3. Elitism - Genetic algorithms often allow the most successful individuals into the next generation unchanged. This cannot be easily imitated by nature.
  4. Life - Merriam-Webster Dictionary[3]
  5. Life - Wiktionary Definition[4] using the biologic version.
    1. Life (Biological Version) - Uses adaptation, growth, metabolism, reaction to stimuli, reproduction, and reproductive mutation.


Decibel (dB)[5] The decibel is used to describe a change in power levels. It measures the ratio between the output power \((P_{o})\) to the input power \((P_{i})\) times \(\log_{10}\). As shown in the formula below: $$\textbf{dB}=\log_{10} \left ( \frac{P_{o}}{P_{i}} \right )$$


  1. (/x + ?) strategy - See plus strategy.
  2. (/x. ?) strategy - See comma strategy.
  3. Adaptation - This denotes the general advantage in ecological or physiological efficiency of an individual in contrast to other members of the population, and it also denotes the process of attaining this state.
  4. Adaptive Behavior - The underlying mechanisms to allow living organisms, and, potentially, robots, to adapt and survive in uncertain environments (cf adaptation).
  5. Adaptive Surface - Possible biological trait combinations in a population of individuals define points in a high-dimensional sequence space, where each coordinate axis corresponds to one of these traits. An additional dimension characterizes the fitness values for each possible trait combination, resulting in a highly multimodal fitness landscape, the so-called adaptive surface or adaptive topography.
  6. Allele[6] - Variant on a gene that occurs at a specified chromosomal position (locus). Some are known to affect disease susceptibility.
  7. Behavior - The response of an organism to the present environmental stimulus. The collection of behaviors of an organism defines the fitness of the organism to its present environment.
  8. Boltzmann Selection - The Boltzmann selection method transfers the probabilistic acceptance criterion of simulated annealing to evolutionary algorithms. The method operates by creating an offspring individual from two parents and accepting improvements (with respect to the parent's fitness) in any case and deteriorations according to an exponentially decreasing function of an exogeneous "temperature" parameter.
  9. Building Block - Certain forms of recombination in evolutionary algorithms attempt to bring together building blocks, shorter pieces of an overall solution, in the hope that together these blocks will lead to increased performance.
  10. Central Dogma - The fact that. b> means of translation and transcription processes, the genetic information is passed from the genotype to the phenotype (i.e. from DNA to RNA and to the proteins). The dogma implies that behaviorally acquired characteristics of an individual are not inherited to its offspring (cf Lamarckism).
  11. Chromatids - The two identical parts of a duplicated chromosome.
  12. Chromosome - Rod-shaped bodies in the nucleus of eukaryotic cells, which contain the hereditary units or genes.
  13. Classifier Systems - Dynamic, rule-based systems capable of learning by examples and induction. Classifier systems evolve a population of production rules (in the so-called Michigan approach, where an individual corresponds to a single rule) or a population of production rule bases (in the so-called Pittsburgh approach, where an individual represents a complete rule base) by means of an evolutionary algorithm. The rules are often encoded by a ternary alphabet, which contains a 'don't care' symbol facilitating a generalization capability of condition or action parts of a rule, thus allowing for an inductive learning of concepts. In the Michigan approach, the rule fitness (its strength) is incrementally updated at each generation by the 'bucket brigade' credit assignment algorithm based on the reward the system obtains from the environment, while in the Pittsburgh approach the fitness of a complete rule base can be calculated by testing the behavior of the individual within its environment.
  14. Codon - A group of three nucleotide bases within the DNA that encodes a single amino acid or start and stop information for the transcription process.
  15. Coevolutionary System - In revolutionary systems, different populations interact with each other in a way such that the evaluation function of one population may depend on the state of the evolution process in the other population(s).
  16. Comma Strategy - The notation (/i, A) strategy describes a selection method introduced in evolution strategies and indicates that a parent population of /u individuals generates A > ? offspring and the best out of these A offspring are deterministically selected as parents of the next generation. (See also Section 25.4.)
  17. Computational Intelligence - The field of computational intelligence is currently seen to include subsymbolic approaches to artificial intelligence, such as neural networks, fuzzy systems, and evolutionary computation. which are gleaned from the model of information processing in natural systems. Following a commonly accepted characterization, a system is computationally intelligent if it deals only with numerical data, does not use knowledge in the classical expert system sense, and exhibits computational adaptivity, fault tolerance, and speed and error rates approaching human performance.
  18. Convergence Reliability - Informally, the convergence reliability of an evolutionary algorithm means its capability to yield reasonably good solutions in the case of highly multimodal topologies of the objective function. Mathematically, this is closely related to the property of global convergence with probability one, which states that, given infinite running time, the algorithm finds a global optimum point with probability one. From a theoretical point of view, this is an important property to justify the feasibility of evolutionary algorithms as global optimization methods.
  19. Convergence Velocity - In the theory of evolutionary algorithms, the convergence velocity is defined either as the expectation of the change of the distance towards the optimum between two subsequent generations, or as the expectation of the change of the objective function value between two subsequent generations. Typically, the best individual of a population is used to define the convergence velocity.
  20. Crossover - A process of information exchange of genetic material that occurs between adjacent chromatids during meiosis.
  21. Cultural algorithm - Cultural algorithms are special variants of evolutionary algorithms which support two models of inheritance, one at the microevolutionary level in terms of traits, and the other at the macroevolutionary level in terms of beliefs. The two models interact via a communication channel that enables the behavior of individuals to alter the belief structure and allows the belief structure to constrain the ways in which individuals can behave. The belief structure represents 'cultural" knowledge about a certain problem and therefore helps in solving the problem on the level of traits.
  22. Cycle Crossover - A crossover operator used in order-based genetic algorithms to manipulate permutations in a permutation preserving way. Cycle crossover performs recombination under the constraint that each element must come from one ^fWil-'or4' the other by transferring element cycles between the mates. The cycle crossover operator preserves absolute positions of the elements of permutations. (See also Section 33.3.)
  23. DNA - Deoxyribonucleic acid, a double-stranded macromolecule of helical structure (comparable to a spiral staircase). Both single strands are linear, unbranched nucleic acid molecules built up from alternating deoxyribose (sugar) and phosphate molecules. Each deoxyribose part is coupled to a nucleotide base, which is responsible for establishing the connection to the other strand of the DNA. The four nucleotide bases adenine (A), thymine (T), cytosine (C) and guanine (G) are the alphabet of the genetic information. The sequences of these bases in the DNA molecule determines the building plan of any organism.
  24. Darwinism - The theory of evolution, proposed by Darwin, that evolution comes about through random variation (mutation) of heritable characteristics, coupled with natural selection, which favors those species for further survival and evolution that are best adapted to their environmental conditions. (See also Chapter 4.)
  25. Deception - Objective functions are called deceptive if the combination of good building blocks by means of recombination leads to a reduction of fitness rather than an increase.
  26. Deficiency - A form of mutation that involves a terminal segment loss of chromosome regions.
  27. Defining Length - The defining length of a schema is the maximum distance between specified positions within the schema. The larger the defining length of a schema, the higher becomes its disruption probability by crossover.
  28. Deletion - A form of mutation that involves an internal segment loss of a chromosome region.
  29. Deme - An independent subpopulation in the migration model of parallel evolutionary algorithms.
  30. Diffusion Model - The diffusion model denotes a massively parallel implementation of evolutionary algorithms, where each individual is realized as a single process being connected to neighboring individuals, such that a spatial individual structure is assumed. Recombination and selection are restricted to the neighborhood of an individual, such that information is locally preserved and spreads only slowly over the population.
  31. Diploid - In diploid organisms, each body cell carries two sets of chromosomes; that is, each chromosome exists in two homologous forms, one of which is phenotypically realized.
  32. Diploidy - The state of having two instances of each chromosome, and thus each gene, in a cell. Organisms with one instance of each chromosome and gene are called haploid.
  33. Discrete Recombination - Discrete recombination works on two vectors of object variables by performing an exchange of the corresponding object variables with probability one half (other settings of the exchange probability are in principle possible) (cf uniform crossover). (See also Section 33.2.)
  34. Duplication - A form of mutation that involves the doubling of a certain region of a chromosome at the expense of a corresponding deficiency on the other of two homologous chromosomes.
  35. Elitism - Elitism is a feature of some evolutionary algorithms ensuring that the maximum objective function value within a population can never reduce from one generation to the next. This can be assured by simply copying the best individual of a population to the next generation, if none of the selected offspring constitutes an improvement of the best value.
  36. Epistasis[7] - One gene dependent on one or more other genes.
  37. Eukaryotic Cell - A cell with a membrane-enclosed nucleus and organelles found in animals, fungi, plants, and protists.
  38. Evolution Strategy - An evolutionary algorithm developed by I Rechenberg and H-P Schwefel at the Technical University of Berlin in the 1960s. The evolution strategy typically employs real-valued parameters, though it has also been used for discrete problems. Its basic features are the distinction between a parent population T&^Mie^) and an offspring population (of size ? > ?), the explicit emphasis on normally distributed mutations, the utilization of different forms of recombination, and the incorporation of the self-adaptation principle for strategy parameters; that is. those parameters that determine the mutation probability density function are evolved on-line, by the same principles which are used to evolve the object variables. (See also Chapter ?.)
  39. Evolutionary Algorithm - See evolutionary computation.
  40. Evolutionary Computation - This encompasses methods of simulating evolution, most often on a computer. The field encompasses methods that comprise a population-based approach that relies on random variation and selection. Instances of algorithms that rely on evolutionary principles are called evolutionary algorithms. Certain historical subsets of evolutionary algorithms include evolution strategies, evolutionary programming, and genetic algorithms.
  41. Evolutionary Operation (EVOP) - An industrial management technique presented by G. E. P. Box in the late fifties, which provides a systematic way to test alternative production processes that result from small modifications of the standard parameter settings. From an abstract point of view, the method resembles a (I + X) strategy with a typical setting of ? = 4 and X = 8 (the so-called 22 and 23 factorial design), and can be interpreted as one of the earliest evolutionary algorithms.
  42. Evolutionary programming - An evolutionary algorithm developed by L. J. Fogel at San Diego, CA, in the 1960s and further refined by D. B. Fogel and others in the 1990s. Evolutionary programming was originally developed as a method to evolve finite-state machines for solving time series prediction tasks and was later extended to parameter optimization problems. Evolutionary programming typically relies on variation operators that are tailored to the problem, and these often are based on a single parent; however, the earliest versions of evolutionary programming considered the possibility for recombining three or more finite-state machines. Selection is a stochastic tournament selection that determines mu individuals to survive out of the mu parents and the mu (or other number of) offspring generated by mutation. Evolutionary programming also uses the self-adaptation principle to evolve strategy parameters on-line during the search (cf evolution strategy). (See also Chapter 10.)
  43. Exon - A region of codons within a gene that is expressed for the phenotype of an organism.
  44. Finite-state Machine - A transducer that can be stimulated by a finite alphabet of input symbols, responds in a finite alphabet of output symbols, and possesses some finite number of different internal states. The behavior of the tinite-state machine is specified by the corresponding input-output symbol pairs and next-state transitions for each input symbol, taken over every state. In evolutionary programming, finite-state machines are historically the first structures that were evolved to tind optimal predictors of the environmental behavior. (See also Chapter 18.)
  45. Fitness - The ability of an individual to survive and reproduce in its natural environment, manifested in its expected number of surviving offspring.
  46. Fitness - The propensity of an individual to survive and reproduce in a particular environment. In evolutionary algorithms, the fitness value of an individual is closely related (and sometimes identical) to the objective function value of the solution represented by the individual, but especially when using proportional selection u scaling function is typically necessary to map objective function values to positive values such that the best-performing individual receives maximum fitness.
  47. Fuzzy System - Fuzzy systems try to model the the fact that real-world circumstances are typically not precise but 'fuzzy'. This is achieved by generalizing the idea of a crisp membership function of sets by allowing for an arbitrary degree of membership in the unit interval. A fuzzy set is then described by such a generalized membership function. Based on membership functions, linguistic variables are defined that capture real-world concepts such as "low temperature". Fuzzy rule-based systems then allow for knowledge processing by means of fuzzification, fuzzy inference, and defuzzification operators which often enable a more realistic modeling of real-world situations than expert systems do.
  48. Gamete - A haploid germ cell that fuses with another in fertilization to form a zygote.
  49. Gene - A unit of codons on the DNA that encodes the synthesis for a protein.
  50. Gene - A unit of heredity and a region of the DNA that encodes a functional product. It is thought that humans have more than 20,000 of these. However, now that coding is known to be far more complex than originally thought, it is no longer clear how to define these units and their boundaries.
  51. Generation Gap - The generation gap characterizes the percentage of the population to be replaced during each generation. The remainder of the population is chosen (at random) to survive intact. The generation gap allows for gradually shifting from the generation-based working scheme towards the extreme of just generating one new individual per 'generation', the so-called steady-state selection algorithm. (See also Chapter 28.)
  52. Genetic Algorithm - An evolutionary algorithm developed by J H Holland and his students at Ann Arbor.TEftMih^Ne 1960s. Fundamentally equivalent procedures were also offered earlier by H J Bremermann at UC Berkeley and A S Fraser at the University of Canberra. Australia in the 1960s and 1950s. Originally, the genetic algorithm or adaptive plan was designed as a formal system for adaptation rather than an optimization system. Its basic features are the strong emphasis on recombination (crossover), use of a probabilistic selection operator (proportional selection), and the interpretation of mutation as a background operator, playing a minor role for the algorithm. While the original form of genetic algorithms (the canonical genetic algorithm) represents solutions by binary strings, a number of variants including real-coded genetic algorithms and order-based genetic algorithms have also been developed to make the algorithm applicable to other than binary search spaces. (See also Chapter 8.)
  53. Genetic Code - The translation process performed by the ribosomes essential!) maps triplets of nucleotide bases to single amino acids. This (redundant) mapping between the 43 = 64 possible codons and the 20 amino acids is the so-called genetic code.
  54. Genetic Drift - A random decrease or increase of biological trait frequencies within the gene pool of a population.
  55. Genetic Programming - Derived from genetic algorithms, the genetic programming paradigm characterizes a class of evolutionary algorithms aiming at the automatic generation of computer programs. To achieve this, each individual of a population represents a complete computer program in a suitable programming language. Most commonly, symbolic expressions representing parse trees in (a subset of) the LISP language are used to represent these programs, but also other representations (including binary representation) and other programming languages (including machine code) are successfully employed. (See also Chapter II.)
  56. Genome - The total genetic information of an organism.
  57. Genotype - The sum of inherited characters maintained within the entire reproducing population. Often also the genetic constitution underlying a single trait or set of traits.
  58. Genotype - The whole or a part of the genetic make-up of an individual or that is common to a group of individuals.
  59. Global Optimization - Given a function f - M —*¦ R, the problem of determining a point x* e M such that f(x") is minimal (i.e. f(x*) < f(x) Va; e M) is called the global optimization problem.
  60. Global Recombination - In evolution strategies, recombination operators are sometimes used which potentially might take all individuals of a population into account for the creation of an offspring individual. Such recombination operators are called global recombination (i.e. global discrete recombination or global intermediate recombination).
  61. Gradient Method - Local optimization algorithms for continuous parameter optimization problems that orient their choice of search directions according to the first partial derivatives of the objective function (its gradient) are called gradient strategies (cf hFll^MniSHg strategy).
  62. Gray Code - A binary code for integer values which ensures that adjacent integers are encoded by binary strings with Hamming distance one. Gray codes play an important role in the application of canonical genetic algorithms to parameter optimization problems, because there are certain situations in which the use of Gray codes may improve the performance of an evolutionary algorithm.
  63. Hamming Distance - For two binary vectors, the Hamming distance is the number of different positions.
  64. Haploid - Haploid organisms carry one set of genetic information.
  65. Heterozygous - An individual having two different alleles of a certain gene.
  66. Heterozygous - Diploid organisms having different alleles for a given trait.
  67. Hillclimbing Strategy - Hillclimbing methods owe their name to the analogy of their way of searching for a maximum with the intuitive way a sightless climber might feel his way from a valley up to the peak of a mountain by steadily moving upwards. These strategies follow a nondecreasing path to an optimum by a sequence of neighborhood moves. In the case of multimodal landscapes, hillclimbing locates the optimum closest to the starting point of its search.
  68. Homologues - Chromosomes of identical structure, but with possibly different genetic information contents.
  69. Homozygous - Diploid organisms having identical alleles for a given trait.
  70. Hybrid Method - Evolutionary algorithms are often combined with classical optimization techniques such as gradient methods to facilitate an efficient local search in the final stage of the evolutionary optimization. The resulting combinations of algorithms are often summarized by the term hybrid methods.
  71. Implicit Parallelism - The concept that each individual solution offers partial information about sampling from other solutions that contain similar subsections. Although it was once believed that maximizing implicit parallelism would increase the efficiency of an evolutionary algorithm. this notion has been proved false in several different mathematical developments (See no-free-lunch theorem).
  72. Individual - A single member of a population. In evolutionary algorithms an individual contains a chromosome or genome, that usually contains at least a representation of a possible solution to the problem being tackled (a single point in the search space). Other information such as certain strategy parameters and the individual's fitness value are usually also stored in each individual.
  73. Intelligence - The definition of the term intelligence for the purpose of clarifying what the essential properties of artificial or computational intelligence should be turns out to be rather complicated. Rather than taking the usual anthropocentric view on this, we adopt a definition by D Fogel which states that intelligence is the capability of a system to adapt its behavior to meet its goals in a range of enWMn'Me'Nts. This definition also implies that evolutionary algorithms provide one possible way to evolve intelligent systems.
  74. Interactive Evolution - The interactive evolution approach involves the human user of the evolutionary algorithm on-line into the variation-selection loop. By means of this method, subjective judgment relying on human intuition, esthetical values, or taste can be utilized for an evolutionary algorithm if a fitness criterion can not be defined explicitly. Furthermore, human problem knowledge can be utilized by interactive evolution to support the search process by preventing unnecessary, obvious detours from the global optimization goal. (See also Chapter 30.)
  75. Intermediate Recombination - Intermediate recombination performs an averaging operation on the components of the two parent vectors. (See also Section 33.2.)
  76. Intron - A region of codons within a gene that do not bear genetic information that is expressed for the phenotype of an organism.
  77. Inversion - A form of mutation that changes a chromosome by rotating an internal segment by 180 and refitting the segment into the chromosome.
  78. Lamarckism - A theory of evolution which preceded Darwin's. Lamarck believed that acquired characteristics of an individual could be passed to its offspring. Although Lamarckian inheritance does not take place in nature, the idea has been usefully applied within some evolutionary algorithms.
  79. Locus - A particular location on a chromosome.
  80. Markov Chain - A Markov process with a finite or countable finite number of states.
  81. Markov Process - A stochastic process (a family of random variables) such that the probability of the process being in a certain state at time ? depends on the state at time ? — 1, not on any states the process has passed earlier. Because the offspring population of an evolutionary algorithm typically depends only on the actual population, Markov processes are an appropriate mathematical tool for the analysis of evolutionary algorithms.
  82. Meiosis - The process of cell division in diploid organisms through which germ cells (gametes) are created.
  83. Metaevolution - The problem of finding optimal settings of the exogeneous parameters of an evolutionary algorithm can itself be interpreted as an optimization problem. Consequently, the attempt has been made to use an evolutionary algorithm on the higher level to evolve optimal strategy parameter settings for evolutionary algorithms, thus hopefully finding a best-performing parameter set that can be used for a variety of objective functions. The corresponding technique is often called a metaevolutionary algorithm. An alternative approach involves the self-adaptation of strategy parameters by evolutionary learning.
  84. Migration - The transfer of an indl^ftfukFffom one subpopulation to another.
  85. Migration Model - The migration model (often also referred to as the island model) is one of the basic models of parallelism exploited by evolutionary algorithm implementations. The population is no longer panmictic, but distributed into several independent subpopulations (so-called demes). which coexist (typically on different processors, with one subpopulation per processor) and may mutually exchange information by interdeme migration. Each of the subpopulations corresponds to a conventional (i.e. sequential) evolutionary algorithm. Since selection takes place only locally inside a population, every deme is able to concentrate on different promising regions of the search space, such that the global search capabilities of migration models often exceed those of panmictic populations. The fundamental parameters introduced by the migration principle are the exchange frequency of information, the number of individuals to exchange, the selection strategy for the emigrants, and the replacement strategy for the immigrants.
  86. Monte Carlo Algorithm - See uniform random search.
  87. Multiarmed Bandit - Classical analysis of schema processing relied on an analogy to sampling from a number of slot machines (one-armed bandits) in order to minimize expected losses.
  88. Multimembered Evolution Strategy - All variants of evolution strategies that use a parent population size of /u > I and therefore facilitate the utilization of recombination are summarized under the term multimembered evolution strategy.
  89. Multiobjective Optimization - In multiobjective optimization, the simultaneous optimization of several, possibly competing, objective functions is required. The family of solutions to a multiobjective optimization problem is composed of all those elements of the search space sharing the property that the corresponding objective vectors cannot be all simultaneously improved. These solutions are called Pareto optimal.
  90. Multipoint Crossover - A crossover operator which uses a predefined number of uniformly distributed crossover points and exchanges alternating segments between pairs of crossover points between the parent individuals (cf one-point crossover).
  91. Mutation - A change in the hereditary material.
  92. Mutation - A change of the genetic material, either occurring in the germ path or in the gametes (generative) or in body cells (somatic). Only generative mutations affect the offspring. A typical classification of mutations distinguishes gene mutations (a particular gene is changed), chromosome mutations (the gene order is changed by translocation or inversion. or the chromosome number is changed by deficiencies, deletions, or duplications), and genome mutations (the number of chromosomes or genomes is changed). In evolutionary algorithms, mutations are either modeled on the phenotypic level by using normally distributed variations with expectation zero for continuous traits) or on the genotypic level (e.g. by using bit inversions with small probability as an equivalent for nucleotide base changes). (See also Chapter 32.)
  93. Mutation Rate - The probability of the occurrence of a mutation during DNA replication.
  94. Natural Selection - The result of competitive exclusion as organisms till the available finite resource space.
  95. Neural Network - Artificial neural networks try to implement the data processing capabilities of brains on a computer. To achieve this (at least in a very simplified form regarding the number of processing units and their interconnectivity). simple units (corresponding to neurons) are arranged in a number of layers and allowed to communicate via weighted connections (corresponding to axons and dendrites). Working (at least principally) in parallel, each unit of the network typically calculates a weighted sum of its inputs, performs some internal mapping ol the result, and eventually propagates a nonzero value to its output connection. Though the artificial models are strong simplifications of the natural model, impressive results have been achieved in a variety of application fields.
  96. Niche - Adaptation of a species occurs with respect to any major kind of environment, the adaptive zone of this species. The set of possible environments that permit survival of a species is called its (ecological) niche.
  97. Niching Methods - In evolutionary algorithms, niching methods aim at the formation and maintenance of stable subpopulations (niches) within a single population. One typical way to achieve this proceeds by means of fitness sharing techniques.
  98. No-free-lunch theorem - This theorem proves that when applied across all possible problems, all algorithms that do not resample points from the search space perform exactly the same on average. This result implies that it is necessary to tune the operators of an evolutionary algorithm to the problem at hand in order to perform optimally, or even better than random search. The no-free-lunch theorem has been extended to apply to certain subsets of all possible problems. Related theorems have been developed indicating that
  99. Object Variables - The parameters that are directly involved in the calculation of the objective function value of an individual.
  100. Off-line Performance - A performance measure for genetic algorithms, giving the average of the best fitness values found in a population over the course of the search.
  101. On-line Performance - A performance measure giving the average fitness over all tested search points over the course of the search.
  102. One-Fifth (1/5) success rule - A theoretically derived rule for the deterministic adjustment of the standard deviation of the mutation operator in a A + I) evolution strategy. The 1/5 success rule reflects the theoretical result that, in order to maximize the convergence vel&Myl-'oh1 average one out of five mutations should cause an improvement with respect to the objective function value. (See also Chapter 9.)
  103. One-point Crossover - A crossover operator using exactly one crossover point on the genome.
  104. Ontogenesis - The development of an organism from the fertilized zygote until its death.
  105. Order - The order of a schema is given by the number of specified positions within the schema. The larger the order of a schema, the higher becomes its probability of disruption by mutation.
  106. Order Crossover - A crossover operator used in order-based genetic algorithms to manipulate permutations in a permutation preserving way. The order crossover (OX) starts in a way similar to partially matched crossover by picking two crossing sites uniformly at random along the permutations and mapping each string to constituents of the matching section of its mate. Then, however, order crossover uses a sliding motion to fill the holes left by transferring the mapped positions. This way. order crossover preserves the relative positions of elements within the permutation. (See also Section 33.3.)
  107. Order Statistics - Given independent random variables with a common probability density function, their arrangement in nondecreasing order is called the order statistics of these random variables. The theory of order statistics provides many useful results regarding the moments (and other properties) of the members of the order statistics. In the theory of evolutionary algorithms, the order statistics are widely utilized to describe deterministic selection schemes such as the comma strategy and tournament selection.
  108. Order-Based Problems - A class of optimization problems that can be characterized by the search for an optimal permutation of specific items. Representative examples of this class are the traveling salesman problem or scheduling problems. In principle, any of the existing evolutionary algorithms can be reformulated for order-based problems, but the first permutation applications were handled by so-called order-based genetic algorithms, which typically use mutation and recombination operators that ensure that the result of the application of an operator to a permutation is again a permutation.
  109. Panmictic Population - A mixed population, in which any individual may be mated with any other individual with a probability that depends only on fitness. Most conventional evolutionary algorithms have panmictic populations.
  110. Parse tree - The syntactic structure of any program in computer programming languages can be represented by a so-called parse tree, where the internal nodes of the tree correspond td~fe^rkftNs and leaves of the tree correspond to constants. Parse trees (or, equivalently. S-expressions) are the fundamental data structure in genetic programming, where recombination is usually implemented as a subtree exchange between two different parse trees. (See also Chapter 19.)
  111. Partially Matched Crossover - A crossover operator used to manipulate permutations in a permutation preserving way. The partially matched crossover (PMX) picks two crossing sites uniformly at random along the permutations, thus defining a matching section used to effect a cross through position-by-position exchange operations. (See also Section 33.3.)
  112. Penalty Function - For constraint optimization problems, the penalty function method provides one possible way to try to achieve feasible solutions - the unconstrained objective function is extended by a penalty function that penalizes infeasible solutions and vanishes for feasible solutions. The penalty function is also typically graded in the sense that the closer a solution is to feasibility, the smaller is the value of the penalty term for that solution. By means of this property, an evolutionary algorithm is often able to approach the feasible region although initially all members of the population might be infeasible.
  113. Phenotype - The behavioral expression of the genotype in a specific environment.
  114. Phenotype - The characteristics of an individual other than its genetic code.
  115. Phytogeny - The evolutionary relationships among any group of organisms.
  116. Pleiotropy - The influence of a single gene on several phenotypic features of an organism.
  117. Plus Strategy - The notation (? + ?) strategy describes a selection method introduced in evolution strategies and indicates that a parent population of ? individuals generates ? > /?. offspring and all ? + ? individuals compete directly, such that the ? best out of parents and offspring are deterministically selected as parents of the next generation.
  118. Polygeny - The combined influence of several genes on a single phenotypical characteristic.
  119. Population - A group of individuals that may interact with each other, for example, by mating and offspring production. The typical population sizes in evolutionary algorithms range from one (for A + 1) evolution strategies) to several thousands (for genetic programming).
  120. Prokaryotic Cell - A cell lacking a membrane-enclosed nucleus and organelles.
  121. Proportional Selection - A selection mechanism that assigns selection probabilities in proportion to the relative fitness of an individual. (See also Chapter 23.)
  122. Protein - A multiply folded biological macromolecule consisting of a long chain of amino acids. The metabolic effects of proteins are basically caused by their three-dimensional folded structure (the tertiary structure) as well as their symmetrical structure components (secondary structure), which result from the amino acid order in tirecfiafrrfprimary structure).
  123. Punctuated Crossover - A crossover operator to explore the potential for self-adaptation of the number of crossover points and their positions. To achieve this, the vector of object variables is extended by a crossover mask, where a one bit indicates the position of a crossover point in the object variable part of the individual. The crossover mask itself is subject to recombination and mutation to allow for a self-adaptation of the crossover operator.
  124. RNA - Ribonucleic acid. The transcription process in the cell nucleus generates a copy of the nucleotide sequence on the coding strand of the DNA. The resulting copy is an RNA molecule, a single-stranded molecule which carries information by means of the necleotide bases adenine, cytosine, guanine, and uracil (U) (replacing the thymine in the DNA). The RNA molecule acts as a messenger that transfers information from the cell nucleus to the ribosomes, where the protein synthesis takes place.
  125. Rank-Based Selection - In rank-based selection methods, the selection probability of an individual does not depend on its absolute fitness as in case of proportional selection, but only on its relative fitness in comparison with the other population members - its rank when all individuals are ordered in increasing (or decreasing) order of fitness values. (See also Chapter 25.)
  126. Recombination - In sexual reproduction, taken broadly, this term means that the genetic material of an offspring is a bricolage of the genetic material of its parents, due to both the independent assortment of chromosomes during the halving of chromosome numbers and the crossing over of chromosomes—the shuffling of segments between two corresponding chromosomes (in humans, typically 2–3 per chromosome)—that occur in the generation of gametes. More generally, DNA recombination in the sense of exchange of genetic material between two DNA molecules or between segments of the same DNA molecule is an important part of mutational mechanisms.
  127. Recombination - See crossover.
  128. Scaling Function - A scaling function is often used when applying proportional selection, particularly when needing to treat individuals with non-positive evaluations. Scaling functions typically employ a linear, logarithmic, or exponential mapping. [See also Chapter 23.)
  129. Schema - A schema describes a subset of all binary vectors of fixed length that have similarities at certain positions. A schema is typically specified by a vector over the alphabet @. l.#}. where the # denotes a "wildcard" matching both zero and one.
  130. Schema Theorem - A theorem offered to describe the expected number of instances of a schema that are represented in the next generation of an evolutionary algorithm when proportional selection is used. Although once considered to be a "fundamental' theorem, mathematical results show that the theorem does not hold in general when iterated over more than one generation and that it may not hold when individual solutions have noisy fitness evaluations. Furthermore, the theorem cannot be used to determine which schemata should be recombined in future generations and has little or no predictive power.
  131. Segmented Crossover - A crossover operator which works similarly to multipoint crossover, except that the number of crossover points is not fixed but may vary around an expectation value. This is achieved by a segment switch rate that specifies the probability that a segment will end at any point in the string.
  132. Selection - The operator of evolutionary algorithms, modeled after the principle of natural selection, which is used to direct the search process towards better regions of the search space by giving preference to individuals of higher fitness for mating and reproduction. The most widely used selection methods include the comma and plus strategies, ranking selection, proportional selection, and tournament selection. {See also Chapters 22-30.)
  133. Self-Adaptation - The principle of self-adaptation facilitates evolutionary algorithms learning their own strategy parameters on-line during the search, without any deterministic exogeneous control, by means of evolutionary processes in the same way as the object variables are modified. More precisely, the strategy parameters (such as mutation rates, variances, or covariances of normally distributed variations) are part of the individual and undergo mutation (recombination) and selection as the object variables do. The biological analogy consists in the fact that some portions of the DNA code for mutator genes or repair enzymes - that is. some partial control over the DNA's mutation rate is encoded in the DNA.
  134. Sharing - Sharing (short for fitness sharing) is a niching method that derates the fitnesses of population elements according to the number of individuals in a niche, so that the population ends up distributed across multiple niches. Simulated annealing - An optimization strategy gleaned from the model of thermodynamic evolution, modeling an annealing process in order to reach a state of minimal energy (where energy is the analogue of fitness in evolutionary algorithms). The strategy works with one trial solution and generates a new solution by means of a variation (or mutation) operator. The new solution is always accepted if it represents a decrease of energy, and it is also accepted with a certain parameter-controlled probability if it represents an increase of energy. The control parameter (or strategy parameter) is commonly called temperature and makes the thermodynamic origin of the strategy obvious.
  135. Speciation - The process whereby a new species comes about. The most common cause of speciation is that of geographical isolation. If a subpopulation of a single species is separated geographically from the main population for a sufficiently long time, its genes will diverge (either due to differences in selection pressures in different locations, or simply due to genetic drift). Eventually, genetic differences will be so great that members of the subpopulation must be considered as belonging to a different (and new) species.
  136. Species - A population of similarly constructed organisms, capable of producing fertile offspring. Members of one species occupy the same ecological niche.
  137. Steady-State Selection - A selection scheme which does not use a generation-wise replacement of the population, but rather replaces one individual per iteration of the main recombine-mutate-select loop of the algorithm. Usually, the worst population member is replaced by the result of recombination and mutation, if the resulting individual represents a fitness improvement compared to the worst population member. The mechanism corresponds to ? (? + 1) selection method in evolution strategies (cf plus strategy).
  138. Strategy Parameter - The control parameters of an evolutionary algorithm are often referred to as strategy parameters. The particular setting of strategy parameters is often critical to gain good performance of an evolutionary algorithm, and the usual technique of empirically searching for an appropriate set of parameters is not generally satisfying. Alternatively, some researchers try techniques of metaevolution to optimize the strategy parameters, while in evolution strategies and evolutionary programming the technique of self-adaptation is successfully used to evolve strategy parameters in the same sense as object variables are evolved.
  139. Takeover Time - A characteristic value to measure the selective pressure of selection methods utilized in evolutionary algorithms. It gives the expected number of generations until, under repeated application of selection as the only operator acting on a population, the population is completely filled with copies of the initially best individual. The smaller the takeover time of a selection mechanism, the higher is its emphasis on reproduction of the best individual, i.e. its selective pressure.
  140. Tournament Selection - Tournament selection methods share the principle of holding tournaments between a number of individuals and selecting the best member of a tournament group for survival to the next generation. The tournament members are typically chosen uniformly at random, and the tournament sizes (number of individuals involved per tournament) are typically small, ranging from two to ten individuals. The tournament process is repeated /j, times in order to select a population of ? members. {See also Chapter 24.)
  141. Transcription - The process of synthesis of a messenger RNA (mRNA) reflecting the structure of a part of the DNA. The synthesis is performed in the cell nucleus.
  142. Translation - The process of synthesis of a protein as a sequence of amino acids according to the information contained in the messenger RNA and the genetic code between triplets of nucleotide bases and amino acids. The synthesis is performed by the ribosomes under utilization of transfer RNA molecules.
  143. Two-membered Evolution Strategy - The two-membered or A + 1) evolution strategy is an evolutionary algorithm working with just one ancestor individual. A descendant is created by means of mutation, and selection selects the better of ancestor and descendant to survive to the next generation (cf plus strategy).
  144. Uniform Random Search - A random search algorithm which samples the search space by drawing points from a uniform distribution over the search space. In contrast to evolutionary algorithms, uniform random search does not update its sampling distribution according to the information gained from past samples, i.e. it is not a Markov process.
  145. Uniform crossover - A crossover operator which was originally defined to work on binary strings. The ERftWrWcVossover operator exchanges each bit with a certain probability between the two parent individuals. The exchange probability typically has a value of one half, but other settings are possible (cf discrete recombination). (See also Section 33.3.)
  146. Zygote - A fertilized egg that is always diploid.


  1. Automatic Programming - The task of finding a program which calculates a certain input-output function. This task has to be performed in automatic programming by another computer program (cf genetic programming). Baldwin effect: Baldwin theorized that individual learning allows an organism to exploit genetic variations that only partially determine a physiological structure. Consequently, the ability to learn can guide evolutionary processes by rewarding partial genetic successes. Over evolutionary time, learning can guide evolution because individuals with useful genetic variations are maintained by learning, such that useful genes are utilized more widely in the subsequent generation. Over time, abilities that previously required learning are replaced by genetically determinant systems. The guiding effect of learning on evolution is referred to as the Baldwin effect.
  2. Evokernel - An OS kernel[8] based on the Micro Kernel architecture by defining each softserver as an environment with digital life software. From modifying code to producing new softservers. This requires redundant softservers running to ensure against failure. Thus larger overhead.
  3. Hardserver - A computer server implemented in hardware.
  4. Mutant OS -- is an Operating System based on a Darwin Machine design.
  5. Softserver - A computer server implemented as a software service. For example, an Oracle instance is a Softserver.

Temporal Mechanics

  1. Drift - A clock that skews forward and backward when measured against a master clock. Drift is always variable.
  2. Master Clock - A theoretically perfect clock. In practice an ultra reliable clock to which other clocks are compared.
  3. Sequential Time - A Time Line where events occur in precise chronological order and cause always comes before effect.
  4. Skew (Backward) - A clock that keeps time but starts running slower. Losing time.
  5. Skew (Forward) - A clock that keeps time but starts running faster. Gaining time.
  6. Synchronization - How close two different clocks are to each other at any given instant.
  7. Syntonization - How accurately a given clock keeps time when compared to an external clock.
"Time is an Illusion: Lunchtime doubly so." ACM January 2016, p.50-55

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