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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 207 (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.
A Survey of Parallel Genetic Algorithms
 CALCULATEURS PARALLELES, RESEAUX ET SYSTEMS REPARTIS
, 1998
"... Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey att ..."
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Cited by 147 (5 self)
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Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multipopulation parallel GAs and presents some recent advancements.
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 114 (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...
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 Proceedings: Ja ..."
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Cited by 73 (8 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 Summary of Research on Parallel Genetic Algorithms
, 1995
"... The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publi ..."
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Cited by 63 (2 self)
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The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic...
Toward More Powerful Recombinations
 Proceedings of the 6 th International Conference on Genetic Algorithms
, 1995
"... This paper suggests a flexible framework for ndimensional crossover, consisting of cutting, classification, and copying of genes. We prove that under this framework, any cutting strategy generates two equivalence classes of genes, making the framework appropriate as a crossover scheme. Three nota ..."
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Cited by 21 (9 self)
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This paper suggests a flexible framework for ndimensional crossover, consisting of cutting, classification, and copying of genes. We prove that under this framework, any cutting strategy generates two equivalence classes of genes, making the framework appropriate as a crossover scheme. Three notable features of this framework are: (i) it enables more effective use of genes' geographical linkage, (ii) it enables more diverse ways of cutting than traditional multipoint crossover on linear strings or existing 2dimensional crossover schemes, and (iii) it can be readily used within most existing genetic algorithm implementations, i.e., the underlying problem need not be inherently multidimensional. We provide guidelines for designing a crossover strategy under this framework, along with two example crossovers. Experimental results show that one can design new crossovers under this framework which outperform traditional crossover on linear strings, uniform crossover, and existing twodi...
Application of a Hybrid Genetic Algorithm to Airline Crew Scheduling
 Computers & Operations Research
, 1996
"... This paper discusses the development and application of a hybrid genetic algorithm to airline crew scheduling problems. The hybrid algorithm consists of a steadystate genetic algorithm and a local search heuristic. The hybrid algorithm was tested on a set of forty realworld problems. It found the ..."
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Cited by 20 (0 self)
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This paper discusses the development and application of a hybrid genetic algorithm to airline crew scheduling problems. The hybrid algorithm consists of a steadystate genetic algorithm and a local search heuristic. The hybrid algorithm was tested on a set of forty realworld problems. It found the optimal solution for half the problems, and good solutions for nine others. The results were compared to those obtained with branchandcut and branchand bound algorithms. The branchandcut algorithm was significantly more successful than the hybrid algorithm, and the branchandbound algorithm slightly better. 1 Introduction Genetic algorithms (GAs) are search algorithms that were developed by John Holland [17]. They are based on an analogy with natural selection and population genetics. One common application of GAs is for finding approximate solutions to difficult optimization problems. In this paper we describe the application of a hybrid GA (a genetic algorithm combined with a local s...
A Genetic Algorithm for the Set Partitioning Problem
, 1995
"... In this paper we present a genetic algorithmbased heuristic for solving the set partitioning problem. The set partitioning problem is an important combinatorial optimisation problem used by many airlines as a mathematical model for flight crew scheduling. We develop a steadystate genetic algori ..."
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Cited by 17 (0 self)
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In this paper we present a genetic algorithmbased heuristic for solving the set partitioning problem. The set partitioning problem is an important combinatorial optimisation problem used by many airlines as a mathematical model for flight crew scheduling. We develop a steadystate genetic algorithm in conjunction with a specialised heuristic feasibility operator for solving the set partitioning problem. Some basic genetic algorithm components, such as fitness definition, parent selection and population replacement are modified. The performance of our algorithm is evaluated on a large set of realworld set partitioning problems provided by the airline industry. Computational results show that the genetic algorithmbased heuristic is capable of producing highquality solutions. In addition a number of the ideas presented (separate fitness, unfitness scores and subgroup population replacement) are applicable to any genetic algorithm for constrained problems. Keywords: combinator...
Advanced Search Techniques For Circuit Partitioning
 In DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1994
"... . Most real world problems especially circuit layout and VLSI design are too complex for any single processing technique to solve in isolation. Stochastic, adaptive and local search approaches have strengths and weaknesses and should be viewed not as competing models but as complimentary ones. This ..."
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Cited by 15 (3 self)
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. Most real world problems especially circuit layout and VLSI design are too complex for any single processing technique to solve in isolation. Stochastic, adaptive and local search approaches have strengths and weaknesses and should be viewed not as competing models but as complimentary ones. This paper describes the application of a combined Tabu Search [1] and Genetic Algorithm heuristic to guide an efficient interchange algorithm to explore and exploit the solution space of a hypergraph partitioning problem. Results obtained indicate, that the generated solutions and running time of this hybrid are superior to results obtained from a combined eigenvector and node interchange method [11]. 1. Introduction In the combinatorial sense, the layout problem is a constrained optimization problem. We are given a description of a circuit (usually called a netlist) which is a description of switching elements and their connecting wires. We seek an assignment of geometric coordinates of the ci...