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30
An Overview of Evolutionary Computation
, 1993
"... Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are current ..."
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Cited by 106 (5 self)
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Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computerbased problem solving systems. In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest. Important similarities and differences are noted, which lead to a discussion of important issues that need to be resolved, and items for future research.
The Equation for the Response to Selection and Its Use for Prediction
, 1997
"... The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the standard d ..."
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Cited by 103 (15 self)
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The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population 's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA , and the average fitness. We compare Mendelian twoparent recombination with genepool recombination, which belongs to a special class of genetic ...
The Schema Theorem and Price's Theorem
 FOUNDATIONS OF GENETIC ALGORITHMS
, 1995
"... Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications f ..."
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Cited by 93 (3 self)
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Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the Schema Theorem has no implications for how well a GA is performing. Interpretations of the Schema Theorem have implicitly assumed that a correlation exists between parent and offspring fitnesses, and this assumption is made explicit in results based on Price's Covariance and Selection Theorem. Schemata do not play a part in the performance theorems derived for representations and operators in general. However, schemata reemerge when recombination operators are used. Using Geiringer's recombination distribution representation of recombination operators, a "missing" schema theorem is derived which makes explicit the intuition for when a GA should perform well. Finally, the method of "adaptive landscape" analysis is exa...
Crossover or Mutation?
 Foundations of Genetic Algorithms 2
, 1992
"... Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mu ..."
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Cited by 70 (3 self)
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Genetic algorithms rely on two genetic operators  crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.
Schemata evolution and building blocks
 Evolutionary Computation
, 1999
"... In the light of a recently derived evolution equation for genetic algorithms we consider the schema theorem and the building block hypothesis. We derive a schema theorem based on the concept of effective fitness showing that schemata of higher than average effective fitness receive an exponentially ..."
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Cited by 63 (12 self)
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In the light of a recently derived evolution equation for genetic algorithms we consider the schema theorem and the building block hypothesis. We derive a schema theorem based on the concept of effective fitness showing that schemata of higher than average effective fitness receive an exponentially increasing number of trials over time. The equation makes manifest the content of the building block hypothesis showing how fit schemata are constructed from fit subschemata. However, we show that generically there is no preference for short, loworder schemata. In the case where schema reconstruction is favored over schema destruction large schemata tend to be favored. As a corollary of the evolution equation we prove Geiringer’s theorem.
Adapting Crossover in Evolutionary Algorithms
 Proceedings of the Fourth Annual Conference on Evolutionary Programming
, 1995
"... One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mut ..."
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Cited by 62 (0 self)
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One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be selfadaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can a...
SEARCH, polynomial complexity, and the fast messy genetic algorithm
, 1995
"... Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Si ..."
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Cited by 50 (10 self)
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Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortunately, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: 1. present SEARCH (Search Envisioned As Relation & Class Hierarchizing)an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; 2. design and testing of the fast messy genetic algorithm. SEARCH is a general framework for understanding blackbox optimization in terms of relations,
Rulebased Evolutionary Online Learning Systems: LEARNING BOUNDS, CLASSIFICATION, AND PREDICTION
, 2004
"... Rulebased evolutionary online learning systems, often referred to as Michiganstyle learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the genera ..."
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Cited by 41 (9 self)
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Rulebased evolutionary online learning systems, often referred to as Michiganstyle learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCSbased learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding
Recombination and Error Thresholds in Finite Populations
, 1999
"... This paper introduces the notions of `quasispecies' and `error threshold' from molecular evolutionary biology. The error threshold is a critical mutation rate beyond which the effect of selection on the population changes drastically. We reproduce, using GAs  and hence finite populations  som ..."
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Cited by 16 (5 self)
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This paper introduces the notions of `quasispecies' and `error threshold' from molecular evolutionary biology. The error threshold is a critical mutation rate beyond which the effect of selection on the population changes drastically. We reproduce, using GAs  and hence finite populations  some interesting results obtained with an analytical model  using infinite populations  from the evolutionary biology literature. A reformulation of a previous analytical expression , which explicitly indicates the extent of the reduction in the error threshold as we move from infinite to finite populations, is derived. Error thresholds are shown to be lower for finite populations. Moreover, as in the infinite case, for low mutation rates recombination can reduce the diversity of the population and enhance overall fitness. For high mutation rates, however, recombination can push the population over the error threshold, and thereby cause a loss of genetic information. These results may be ...
Exact Schema Theory for GP and Variablelength GAs with Homologous Crossover
 In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001
, 2001
"... In this paper we present a new exact schema theory for genetic programming and variablelength genetic algorithms which is applicable to the general class of homologous crossovers. These are a group of operators, including GP onepoint crossover and GP uniform crossover, where the offspring ar ..."
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Cited by 12 (8 self)
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In this paper we present a new exact schema theory for genetic programming and variablelength genetic algorithms which is applicable to the general class of homologous crossovers. These are a group of operators, including GP onepoint crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. The theory is based on the concepts of GP crossover masks and GP recombination distributions (both introduced here for the first time), as well as the notions of hyperschema and node reference systems introduced in other recent research. This theory generalises and refines previous work in GP and GA theory.