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33
Evolutionary computation: Comments on the history and current state
 IEEE Transactions on Evolutionary Computation
, 1997
"... Abstract — 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 struc ..."
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Cited by 207 (0 self)
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Abstract — 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. Index Terms — Classifier systems, evolution strategies, evolutionary computation, evolutionary programming, genetic algorithms,
SelfAdaptation in Genetic Algorithms
 Proceedings of the First European Conference on Artificial Life
, 1992
"... Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time. In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are chang ..."
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Cited by 115 (2 self)
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Within Genetic Algorithms (GAs) the mutation rate is mostly handled as a global, external parameter, which is constant over time or exogeneously changed over time. In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment dependent selfadaptation of appropriate settings for the mutation rate is possible even for GAs. Furthermore, the reduction of the number of external parameters of a GA is seen as a first step towards achieving a problemdependent selfadaptation of the algorithm. Introduction Natural evolution has proven to be a powerful mechanism for emergence and improvement of the living beings on our planet by performing a randomized search in the space of possible DNAsequences. Due to this knowledge about the qualities of natural evolution, some resea...
Drift analysis and average time complexity of evolutionary algorithms
 Artificial Intelligence
, 2001
"... The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including cond ..."
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Cited by 75 (25 self)
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The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including conditions under which an EA will take no more than polynomial time (in problem size) to solve a problem and conditions under which an EA will take at least exponential time (in problem size) to solve a problem. The paper first presents the general results, and then uses several problems as examples to illustrate how these general results can be applied to concrete problems in analyzing the average time complexity of EAs. While previous work only considered (1 + 1) EAs without any crossover, the EAs considered in this paper are fairly general, which use a finite population, crossover, mutation, and selection.
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
 Foundation of Intelligent Systems 9th International Symposium, ISMIS '96
, 1996
"... . The role of the mutation rate in canonical genetic algorithms is investigated by comparing a constant setting, a deterministically varying, timedependent mutation rate schedule, and a selfadaptation mechanism for individual mutation rates following the principle of selfadaptation as used in evo ..."
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Cited by 54 (0 self)
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. The role of the mutation rate in canonical genetic algorithms is investigated by comparing a constant setting, a deterministically varying, timedependent mutation rate schedule, and a selfadaptation mechanism for individual mutation rates following the principle of selfadaptation as used in evolution strategies. The power of the selfadaptation mechanism is illustrated by a timevarying optimization problem, where mutation rates have to adapt continuously in order to follow the optimum. The strengths of the proposed deterministic schedule and the selfadaptation method are demonstrated by a comparison of their performance on difficult combinatorial optimization problems (multiple knapsack, maximum cut and maximum independent set in graphs). Both methods are shown to perform significantly better than the canonical genetic algorithm, and the deterministic schedule yields the best results of all control mechanisms compared. 1 Introduction Genetic Algorithms [11, 14] are the best kno...
Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
, 1994
"... We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11mult ..."
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Cited by 46 (0 self)
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We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA. Department of Computer Science, University of California at Berkeley. Supported by a NASA Graduate Fellowship. This paper was written while the author was a visiting researcher at the Ecole Normale Sup'erieurerue d'Ulm, Group...
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
 IEEE Transactions on Evolutionary Computation
, 2005
"... Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may ..."
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Cited by 32 (7 self)
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Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions. Index Terms—Cellular genetic algorithm (cGA), evolutionary algorithm (EA), dynamic adaptation, neighborhoodtopopulation ratio. I.
Solving The Simple Plant Location Problem By Genetic Algorithm
 RAIRO Operations Research
, 2001
"... The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorit ..."
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Cited by 21 (1 self)
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The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.
Evolution Strategies: an alternative evolutionary algorithm
 Artificial Evolution
, 1995
"... In this paper, evolution strategies (ESs) a class of evolutionary algorithms using normally distributed mutations, recombination, deterministic selection of the � � ? 1 best offspring individuals, and the principle of selfadaptation for the collective online learning of strategy parameters ..."
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Cited by 20 (0 self)
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In this paper, evolution strategies (ESs) a class of evolutionary algorithms using normally distributed mutations, recombination, deterministic selection of the � � ? 1 best offspring individuals, and the principle of selfadaptation for the collective online learning of strategy parameters are described by demonstrating their differences to genetic algorithms. By comparison of the algorithms, it is argued that the application of canonical genetic algorithms for continuous parameter optimization problems implies some difficulties caused by the encoding of continuous object variables by binary strings and the constant mutation rate used in genetic algorithms. Because they utilize a problemadequate representation and a suitable selfadaptive step size control guaranteeing linear convergence for strictly convex problems, evolution strategies are argued to be more adequate for continuous problems. The main advantage of evolution strategies, the selfadaptation of strategy parameters, is explained in detail, and further components such as recombination and selection are described on a rather general level. Concerning theory, recent results regarding convergence velocity and global convergence of evolution strategies are briefly summarized, especially including the results for (��,)ESs with recombination. It turns out that the theoretical ground of ESs provides many more results about their behavior as optimization algorithms than available for genetic algorithms, and that ESs have all properties required for global optimization methods. The paper concludes by emphasizing the necessity for an appropriate step size control and the recommendation to avoid encoding mappings by using a problemadequate representation of solutions within evolutionary algorithms.
MALLBA: A library of skeletons for combinatorial optimisation
, 2002
"... The mallba project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++. MALLBA offers three families of generic resolution methods: exact,heuristic and hybrid. Moreover, for each resolution method, MALLBA provides three different implementatio ..."
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Cited by 18 (8 self)
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The mallba project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++. MALLBA offers three families of generic resolution methods: exact,heuristic and hybrid. Moreover, for each resolution method, MALLBA provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks (currently under development). This paper shows the architecture of the mallba library, presents some of its skeletons and offers several computational results to show the viability of the approach.
An Evolutionary Heuristic for the Minimum Vertex Cover Problem
 KI94 Workshops (Extended Abstracts
, 1994
"... this paper, are used to compare the behavior of the genetic algorithm with the vercov heuristic. Recall that these graphs contain n = 3k + 4 (k 1) nodes distributed on three levels. They can be scaled up by choosing high values for k. We choose problem instances of the regular graph of sizes n = 10 ..."
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Cited by 16 (0 self)
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this paper, are used to compare the behavior of the genetic algorithm with the vercov heuristic. Recall that these graphs contain n = 3k + 4 (k 1) nodes distributed on three levels. They can be scaled up by choosing high values for k. We choose problem instances of the regular graph of sizes n = 100 (k = 32) and n = 202 (k = 66). For each of the problems a total of N = 100 independent runs of the vercov heuristic is performed, and the results are summarized in table 2. The same experiments were also performed for graphs of size n = 200 in order to test the behavior of the genetic algorithm as well as the vercov heuristic for an even larger problem size. In this case, the genetic algorithm was allowed to run for 4 \Delta 10