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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 128 (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.
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 78 (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.
A Measure of Landscapes
 Evolutionary Computation
, 1995
"... The structure of a fitness landscape is still an illdefined concept. This paper introduces a statistical fitness landscape analysis, that can be used on a multitude of fitness landscapes. The result of this analysis is a statistical model that, together with some statistics denoting the explanatory ..."
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Cited by 62 (3 self)
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The structure of a fitness landscape is still an illdefined concept. This paper introduces a statistical fitness landscape analysis, that can be used on a multitude of fitness landscapes. The result of this analysis is a statistical model that, together with some statistics denoting the explanatory and predictive value of this model, can serve as a measure for the structure of the landscape. The analysis is based on a statistical time series analysis known as the BoxJenkins approach, that, among others, estimates the autocorrelations of a time series of fitness values generated by a random walk on the landscape. From these estimates, a correlation length for the landscape can be derived. Keywords: Fitness landscapes, Correlation structure, Correlation length 1 Introduction "We need a real theory relating the structure of rugged multipeaked fitness landscapes to the flow of a population upon those landscapes. We do not yet have such a theory." This quote, from Stuart A. Kauffman [...
Fitness landscapes and difficulty in genetic programming
 In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence. IEEE
, 1994
"... ..."
Correlation Analysis of the SynchronizingCA Landscape
 Physica D
, 1997
"... this paper was to show that the in [6] proposed landscape analysis will still work nicely on less well behaved, nonisotropic landscapes. This turns out to be true, as long as subspaces in the landscape can be identified that are locally isotropic enough to produce good results. Identifying these su ..."
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Cited by 13 (6 self)
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this paper was to show that the in [6] proposed landscape analysis will still work nicely on less well behaved, nonisotropic landscapes. This turns out to be true, as long as subspaces in the landscape can be identified that are locally isotropic enough to produce good results. Identifying these subspaces will generally be the most difficult part, but once they are found, the landscape analysis can be applied in a straightforward way.
Blending Heuristics with a PopulationBased Approach: A "Memetic" Algorithm for the Traveling Salesman Problem
 REPORT 9212, UNIVERSIDAD NACIONAL DE LA PLATA, C.C. 75, 1900 LA PLATA
, 1994
"... Very recently many researchers, with backgrounds in parallel computing, started to develop hybrids of traditional genetic algorithms. The main departure from standard genetic algorithms is that these new methods incorporate specific heuristics for the problem at hand (drawing on a tradition which ha ..."
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Cited by 10 (4 self)
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Very recently many researchers, with backgrounds in parallel computing, started to develop hybrids of traditional genetic algorithms. The main departure from standard genetic algorithms is that these new methods incorporate specific heuristics for the problem at hand (drawing on a tradition which has roots outside the genetic framework) and which we apply within a stochastic game that exerts a selective pressure. The heuristics are used for periods of individual optimization, that is when agents do not interact. New computational results for the Traveling Salesman Problem will be presented in this paper. The approach is prepared to include Tabu Search techniques, introducing a new crossover operator (which is called Random Respectful Corner Recombination) and a special pair of a topology and set of rules for the interaction between agents. The approach has a natural parallelism and a feature called superlinear speedup will also be discussed.
Population Flow on Fitness Landscapes
, 1994
"... Contents 1 Introduction 1 1.1 The goal of this thesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 The outline of the thesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.3 Acknowledgements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 Fitness L ..."
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Cited by 8 (2 self)
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Contents 1 Introduction 1 1.1 The goal of this thesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 The outline of the thesis : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.3 Acknowledgements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 Fitness Landscapes 5 2.1 The concept of fitness : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.1.1 Fitness in biology : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.1.2 Fitness in problem solving : : : : : : : : : : : : : : : : : : : : : : : 6 2.1.3 The fitness function : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 2.2 Fitness landscapes : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 2.2.1 Bit strings and Hamming distance : : : : : : : : : : : : : : : : : : : 8 2.2.2 The
Indexed bibliography of distributed genetic algorithms
 University of Vaasa, Department of
, 1995
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