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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,
An Approach To A Problem In Network Design Using Genetic Algorithms
, 1995
"... In the work of communications network design there are several recurring themes: maximizing flows, finding circuits, and finding shortest paths or minimal cost spanning trees, among others. Some of these problems appear to be harder than others. For some, effective algorithms exist for solving them, ..."
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Cited by 31 (0 self)
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In the work of communications network design there are several recurring themes: maximizing flows, finding circuits, and finding shortest paths or minimal cost spanning trees, among others. Some of these problems appear to be harder than others. For some, effective algorithms exist for solving them, for others, tight bounds are known, and for still others, researchers have few clues towards a good approach. One of these latter, nastier problems arises in the design of communications networks: the Optimal Communication Spanning Tree Problem (OCSTP). First posed by Hu in 1974, this problem has been shown to be in the family of NPcomplete problems. So far, a good, generalpurpose approximation algorithm for it has proven elusive. This thesis describes the design of a genetic algorithm for finding reliably good solutions to the OCSTP. The genetic algorithm approach was thought to be an appropriate choice since they are computationally simple, provide a powerful parallel search capability...
A RandomKey Genetic Algorithm for the Generalized Traveling Salesman Problem
 European Journal of Operational research
, 2004
"... The Generalized Traveling Salesman Problem is a variation of the well known Traveling Salesman Problem in which the set of nodes is divided into clusters; the objective is to find a minimumcost tour passing through one node from each cluster. We present an effective heuristic for this problem. The ..."
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Cited by 31 (0 self)
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The Generalized Traveling Salesman Problem is a variation of the well known Traveling Salesman Problem in which the set of nodes is divided into clusters; the objective is to find a minimumcost tour passing through one node from each cluster. We present an effective heuristic for this problem. The method combines a genetic algorithm (GA) with a local tour improvement heuristic. Solutions are encoded using random keys, which circumvent the feasibility problems encountered when using traditional GA encodings. On a set of 41 standard test problems with symmetric distances and up to 442 nodes, the heuristic found solutions that were optimal in most cases and were within 1% of optimality in all but the largest problems, with computation times generally within 10 seconds. The heuristic is competitive with other heuristics published to date in both solution quality and computation time.
Survey of Genetic Algorithms and Genetic Programming
 In In Proceedings of the Wescon 95  Conference Record: Microelectronics, Communications Technology, Producing Quality Products, Mobile and Portable Power, Emerging Technologies
, 1995
"... This paper provides an introduction to genetic algorithms and genetic programming and lists sources of additional information, including books and conferences as well as email lists and software that is available over the Internet. 1. GENETIC ALGORITHMS John Holland's pioneering book Adaptation i ..."
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Cited by 25 (0 self)
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This paper provides an introduction to genetic algorithms and genetic programming and lists sources of additional information, including books and conferences as well as email lists and software that is available over the Internet. 1. GENETIC ALGORITHMS John Holland's pioneering book Adaptation in Natural and Artificial Systems (1975, 1992) showed how the evolutionary process can be applied to solve a wide variety of problems using a highly parallel technique that is now called the genetic algorithm. The genetic algorithm (GA) transforms a population (set) of individual objects, each with an associated fitness value, into a new generation of the population using the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The genetic algorithm attempts to find a very good (or best) ...
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 22 (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...
Epistasis in Genetic Algorithms: An Experimental Design Perspective
 Proc. of the 6th International Conference on Genetic Algorithms, (pp 217224
, 1995
"... In an earlier paper we examined the relationship between genetic algorithms (GAs) and traditional methods of experimental design. This was motivated by an investigation into the problems caused by epistasis in the implementation and application of GAs to optimization problems. We showed how t ..."
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Cited by 20 (1 self)
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In an earlier paper we examined the relationship between genetic algorithms (GAs) and traditional methods of experimental design. This was motivated by an investigation into the problems caused by epistasis in the implementation and application of GAs to optimization problems. We showed how this viewpoint enables us to gain further insights into the determination of epistatic effects, and into the value of different forms of encoding a problem for a GA solution. We also demonstrated the equivalence of this approach toWalsh transform analysis. In this paper we consider further the question of whether the epistasis metric actually gives a good prediction of the ease or difficulty of solution of a given problem by a GA. Our original analysis assumed, as does the rest of the related literature, knowledge of the complete solution space. In practice, we only ever sample a fraction of all possible solutions, and this raises significant questions which are the subject of...
Determinant factorization: A new encoding scheme for spanning trees applied to the probabilistic minimum spanning tree problem
 In Eschelman, L. (Ed.), Proceedings of the S9cth International Conference on Genetic Algorithms
, 1995
"... This paper describes a new encoding scheme for the representation of spanning trees. This new encoding scheme is based on the factorization of the determinant of the indegree matrix of the original graph. Each factor represents a spanning tree if the determinant corresponding to that factor is equa ..."
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Cited by 19 (0 self)
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This paper describes a new encoding scheme for the representation of spanning trees. This new encoding scheme is based on the factorization of the determinant of the indegree matrix of the original graph. Each factor represents a spanning tree if the determinant corresponding to that factor is equal to one. Our new determinant encoding will be compared to the Prufer encoding, and to the node and link biased encoding by solving an NPcomplete variation of the minimum spanning tree problem, known as the Probabilistic Minimum Spanning Tree Problem. Given a connected graph G(V,E), a cost function c:E;!< +, and a probability function P:2V;![0 � 1], the problem is to nd an a priori spanning tree of minimum expected length. Our results show a signi cant improvement in using the new determinant encoding and the node and link biased encoding compared to Prufer's encoding. We also show empirically that our new determinant encoding scheme is as good as the node and link biased encoding. Our new determinant encoding works very well for restricted spanning trees, and for incomplete graphs. 1
Interactive Evolution of LSystem Grammars for Computer Graphics Modelling
"... Evolution of Lindenmayer Systems (LSystems) provides a powerful method for creating complex computer graphics and animations. This paper describes an interactive modelling system for computer graphics in which the user is able to "evolve" grammatical rules and surface equations. Starting from any i ..."
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Cited by 17 (2 self)
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Evolution of Lindenmayer Systems (LSystems) provides a powerful method for creating complex computer graphics and animations. This paper describes an interactive modelling system for computer graphics in which the user is able to "evolve" grammatical rules and surface equations. Starting from any initial LSystem grammar the evolution proceeds via repeated random mutation and user selection. Subclasses of the mutation process depend on the context of the current symbol or rule being mutated and include mutation of: parametric equations and expressions, growth functions, rules and productions. As the grammar allows importation of parametric surfaces, these surfaces can be mutated and selected as well. The mutated rules are then interpreted to create a threedimensional, timedependent model composed of parametric and polygonal geometry. LSystem evolution allows with minimal knowledge of LSystems to create complex, "lifelike " images and animations that would be difficult and far more timeconsuming to achieve by writing rules and equations explicitly.
Evolution of Food Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming
, 1992
"... This paper describes the recently developed genetic programming paradigm which genetically breeds a population of computer programs to solve problems. The paper then shows, step by step, how to apply genetic programming to a problem of behavioral ecology in biology – specifically, two versions of th ..."
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Cited by 17 (1 self)
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This paper describes the recently developed genetic programming paradigm which genetically breeds a population of computer programs to solve problems. The paper then shows, step by step, how to apply genetic programming to a problem of behavioral ecology in biology – specifically, two versions of the problem of finding an optimal food foraging strategy for the Caribbean Anolis lizard. A simulation of the adaptive behavior of the lizard is required to evaluate each possible adaptive control strategy considered for the lizard. The foraging strategy produced by genetic programming is close to the mathematical solution for the one version for which the solution is known and appears to be a reasonable approximation to the solution for the second version of the problem.