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139
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 stand ..."
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Cited by 121 (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 ...
Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise
 EVOLUTIONARY COMPUTATION
, 1996
"... This paper analyzes the effect of noise on different selection mechanisms for genetic algorithms. Models for several selection scheme are developed that successfully predict the convergence characteristics of genetic algorithms within noisy environments. The selection schemes modeled in this paper i ..."
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Cited by 116 (8 self)
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This paper analyzes the effect of noise on different selection mechanisms for genetic algorithms. Models for several selection scheme are developed that successfully predict the convergence characteristics of genetic algorithms within noisy environments. The selection schemes modeled in this paper include proportionate selection, tournament selection, ¯ selection, and linear ranking selection. These models are shown to accurately predict the convergence rate of genetic algorithms under a wide range of noise levels.
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 90 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
A Comparison of Selection Schemes used in Evolutionary Algorithms
 Evolutionary Computation
, 1997
"... Evolutionary Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which in this paper a new description model based on fitness distributions is introduced. ..."
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Cited by 83 (2 self)
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Evolutionary Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which in this paper a new description model based on fitness distributions is introduced.
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by th ..."
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Cited by 68 (27 self)
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Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation
Determining Successful Negotiation Strategies: An Evolutionary Approach
, 1998
"... To be successful in open, multiagent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opp ..."
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Cited by 65 (7 self)
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To be successful in open, multiagent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opponent in different environments. In particular, we adopt an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent's opponent. 1. Introduction Negotiation is a central component of many multiagent systems. Agents negotiate to coordinate their activities and to come to mutually acceptable agreements about the division of labour and resources. In many cases, the agents involved are required to exhibit a range of different behaviours in a variety of ...
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
"... This paper investigates how the policy used to select migrants and replacements affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations. ..."
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Cited by 43 (2 self)
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This paper investigates how the policy used to select migrants and replacements affects the selection pressure in parallel evolutionary algorithms (EAs) with multiple populations.
Decision Making in a Hybrid Genetic Algorithm
, 1997
"... There are several issues that need to be taken in consideration when designing a hybrid problem solver. This paper focuses on one of themdecision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use ..."
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Cited by 37 (2 self)
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There are several issues that need to be taken in consideration when designing a hybrid problem solver. This paper focuses on one of themdecision making. More specifically, we address the following questions: given two different methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum efficiency? We present a model for hybridizing genetic algorithms (GAs) based on a concept that decision theorists call probability matching and we use it to combine an elitist selectorecombinative GA with a simple hillclimber (HC). Tests on an easy problem with a small population size match our intuition that both GA and HC are needed to solve the problem efficiently. I. Introduction It is very unlikely that a GA will outperform a specialized scheme tailored to a problem. However, a combination of the two usually performs better than either one alone. This happens because on a hybrid there is the possibility of incorporating do...
Don't Evaluate, Inherit
 Proceedings of the Genetic and Evolutionary Computation Conference, 551–558. (Also IlliGAL
, 2001
"... This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and populationsizing models are derived and compared with experimental results. These models are optimized for greatest speedup and the optimal inheritance proportion ..."
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Cited by 36 (17 self)
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This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and populationsizing models are derived and compared with experimental results. These models are optimized for greatest speedup and the optimal inheritance proportion to obtain such a speedup is derived. Results on OneMax problems show that when the inheritance effects are considered in the populationsizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.
Let’s get ready to rumble: Crossover versus mutation head to head
 In GECCO ’04: Proc. of the Genetic and Evolutionary Computation Conference
, 2004
"... This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search amo ..."
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Cited by 28 (20 self)
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This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for additively separable deterministic problems, the BBwise mutation is more efficient than crossover, while the crossover outperforms the mutation on additively separable problems perturbed with additive Gaussian noise. The results show that the speedup of using BBwise mutation on deterministic problems is O ( √ k log m), where k is the BB size, and m is the number of BBs. Likewise, the speedup of using crossover on stochastic problems with fixed noise variance is O(m √ k / logm). 1