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Evolutionary computation: Comments on the history and current state
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1997
"... Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and ..."
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Cited by 280 (0 self)
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Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.
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 implicati ..."
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Cited by 101 (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...
Deception Considered Harmful
 Foundations of Genetic Algorithms 2
, 1992
"... A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of Deception, defined in terms of the static average fitness of competing schemas. This article examines t ..."
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Cited by 80 (0 self)
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A central problem in the theory of genetic algorithms is the characterization of problems that are difficult for GAs to optimize. Many attempts to characterize such problems focus on the notion of Deception, defined in terms of the static average fitness of competing schemas. This article examines the Static Building Block Hypothesis (SBBH), the underlying assumption used to define Deception. Exploiting contradictions between the SBBH and the Schema Theorem, we show that Deception is neither necessary nor sufficient for problems to be difficult for GAs. This article argues that the characterization of hard problems must take into account the basic features of genetic algorithms, especially their dynamic, biased sampling strategy. Keywords: Deception, building block hypothesis 1 INTRODUCTION Since Holland's early work on the analysis of genetic algorithms (GAs), the usual approach has been to focus on the allocation of search effort to subspaces described by schemas representing hyper...
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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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.
ExplanationBased Learning
, 2004
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are not
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An Alternative Explanation for the Manner in which Genetic Algorithms Operate
 BioSystems
, 1997
"... The common explanation of the manner in which genetic algorithms (GAs) process individuals in a population of contending solutions relies on the "building block hypothesis." This suggests that successively better solutions are generated by combining useful parts of extant solutions. An ..."
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Cited by 31 (10 self)
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The common explanation of the manner in which genetic algorithms (GAs) process individuals in a population of contending solutions relies on the "building block hypothesis." This suggests that successively better solutions are generated by combining useful parts of extant solutions. An alternative explanation is presented which focuses on the collective phenomena taking place in populations that undergo recombination. The new explanation is derived from investigations in evolution strategies (ESs). The principles studied are general, and hold for all evolutionary algorithms (EAs), including genetic algorithms (GAs). Further, they appear to be somewhat analogous to some theories and observations on the benefits of sex in biota. Keywords building block hypothesis, evolutionary algorithms, multirecombination 1 Introduction Although specific theoretical investigations into the properties of genetic algorithms have gained considerable recent attention, there still is no satisfa...
Genetic Invariance: A New Paradigm for Genetic Algorithm Design
, 1992
"... This paper presents some experimental results and analyses of the gene invariant genetic algorithm(GIGA). Although a subclass of the class of genetic algorithms, this algorithm and its variations represent a unique approach with many interesting results. The primary distinguishing feature is that wh ..."
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Cited by 24 (3 self)
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This paper presents some experimental results and analyses of the gene invariant genetic algorithm(GIGA). Although a subclass of the class of genetic algorithms, this algorithm and its variations represent a unique approach with many interesting results. The primary distinguishing feature is that when a pair of offspring are created and chosen as worthy of membership in the population they replace their parents. With no mutation this has the effect of maintaining the original genetic material over time, although it is reorganized. In this paper no mutation is allowed. The only genetic operator used is crossover. Several crossover operators are experimented with and analyzed. The notion of a family is introduced and different selection methods are analyzed. Tests using simple functions, the De Jong five function test suite and several deceptive functions are reported. GIGA performs as well as traditional GAs, and sometimes better. The evidence indicates that this method makes more effec...
Genetic Algorithms and Machine Learning
, 1993
"... One approach to the design of learning systems is to extract heuristics from existing adaptive systems. Genetic algorithms are heuristic learning models based on principles drawn from natural evolution and selective breeding. Some features that distinguish genetic algo rithms from other search meth ..."
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Cited by 23 (0 self)
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One approach to the design of learning systems is to extract heuristics from existing adaptive systems. Genetic algorithms are heuristic learning models based on principles drawn from natural evolution and selective breeding. Some features that distinguish genetic algo rithms from other search methods are: A population of structures that can be interpreted as candidate solutions to the given problem; The competitive selection of structures for reproduction, based on each structure's fitness as a solution to the given problem; Idealized genetic operators that alter the selected structures in order to create new structures for fur ther testing.
A Study Of Crossover Operators In Genetic Programming
, 1991
"... Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have ..."
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Cited by 16 (2 self)
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Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have been a number of contradictory results concerning crossover operators with respect to overall performance. Recently, for example, genetic algorithms were used to design neural network modules and their control circuits. In these studies, a genetic algorithm without crossover outperformed a genetic algorithm with crossover. This report reexamines these studies, and concludes that the results were caused by a small population size. New results are presented that illustrate the effectiveness of crossover when the population size is larger. From a performance view, the results indicate that better neural networks can be evolved in a shorter time if the genetic algorithm uses crossover. 1. Introducti...
On the Dynamics of EAs without Selection
 Foundations of Genetic Algorithms
, 1999
"... This paper investigates the dynamics of evolutionary algorithms (EAs) without fitness based selection (constant fitness). Such algorithms exhibit a behavior similar to the MISR effect (mutationinduced speciation by recombination) which has been found in the analysis of (=D ; ) evolution strategies. ..."
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This paper investigates the dynamics of evolutionary algorithms (EAs) without fitness based selection (constant fitness). Such algorithms exhibit a behavior similar to the MISR effect (mutationinduced speciation by recombination) which has been found in the analysis of (=D ; ) evolution strategies. It will be shown that this behavior can be observed in a variety of EAs, not only in unrestricted search spaces, but also in binary GAs. The quantification of this effect is done by introducing the expected population variance oe 2 P . The evolution of oe 2 P over the time g is analytically calculated for both unrestricted and binary search spaces. The theoretical predictions are compared with experiments. The genetic drift phenomenon and the diffusion effect are derived from the general oe 2 P formulae, and it will be shown that MISR is a finite population size sampling effect which cannot be observed in infinite populations. 1 Introduction The question of how GAs, or more generally...