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93
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,
On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts  Towards Memetic Algorithms
, 1989
"... Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could ..."
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Cited by 186 (10 self)
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Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could possibly enumerate 10 9 tours per second on a computer it would thus take roughly 10 639 years of computing to establish the optimality of this tour by exhaustive enumeration." This quote shows the real difficulty of a combinatorial optimization problem. The huge number of configurations is the primary difficulty when dealing with one of these problems. The quote belongs to M.W Padberg and M. Grotschel, Chap. 9., "Polyhedral computations", from the book The Traveling Salesman Problem: A Guided tour of Combinatorial Optimization [124]. It is interesting to compare the number of configurations of realworld problems in combinatorial optimization with those large numbers arising in Cosmol...
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 169 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Genetic Hybrids for the Quadratic Assignment Problem
 DIMACS Series in Mathematics and Theoretical Computer Science
, 1993
"... . A new hybrid procedure that combines genetic operators to existing heuristics is proposed to solve the Quadratic Assignment Problem (QAP). Genetic operators are found to improve the performance of both local search and tabu search. Some guidelines are also given to design good hybrid schemes. Thes ..."
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Cited by 89 (0 self)
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. A new hybrid procedure that combines genetic operators to existing heuristics is proposed to solve the Quadratic Assignment Problem (QAP). Genetic operators are found to improve the performance of both local search and tabu search. Some guidelines are also given to design good hybrid schemes. These hybrid algorithms are then used to improve on the best known solutions of many test problems in the literature. 1. Introduction The quadratic assignment problem (QAP) can be stated as: min OE2P (n) n X i=1 n X j=1 a ij b OE(i)OE(j) ; where A = (a ij ) and B = (b kl ) are two n \Theta n matrices and P (n) is the set of all permutations of f1; :::; ng. Matrix A is often referred to as a distance matrix between sites, and B as a flow matrix between objects. In most cases, the matrices A and B are symmetrical with a null diagonal. A permutation may then be interpreted as an assignment of objects to sites with a quadratic cost associated to it. There are many applications that can be fo...
Evolutionary Algorithms for Reinforcement Learning
 Journal of Artificial Intelligence Research
, 1999
"... There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are wellknown examples of these approaches. Kaelbling, Littman and Moore recently provided a ..."
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Cited by 83 (1 self)
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There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are wellknown examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problemspecific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications. 1. Introduction Kaelbling, Littman, and Moore (1996) and more recently Sutton and Barto (1998) provide informative surveys of the field of reinforcement learning (RL). They characterize two classes of methods for reinforcement learning: methods that search the space of value fu...
A knowledgeintensive genetic algorithm for supervised learning
, 1993
"... Abstract. Supervised learning in attributebased spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The fullmemory approach developed here uses the same nighlevel descriptive language that i ..."
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Cited by 82 (1 self)
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Abstract. Supervised learning in attributebased spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The fullmemory approach developed here uses the same nighlevel descriptive language that is used in rulebased systems. This allows for an easy utilization of inference rules of the wellknown inductive learning methodology, which replace the traditional domainindependent operators and make the search taskspecific. Moreover, a closer relationship between the underlying task and the processing mechanisms provides a setting for an application of more powerful taskspecific heuristics. Initial results obtained with a prototype implementation for the simplest case of single concepts indicate that genetic algorithms can be effectively used to process nighlevel concepts and incorporate taskspecific knowledge. The method of abstracting the genetic algorithm to the problem level, described here for the supervised inductive learning, can be also extended to other domains and tasks, since it provides a framework for combining recently popular genetic algorithm methods with traditional problemsolving methodologies. Moreover, in this particular case, it provides a very powerful tool enabling study of the widely accepted but not so well understood inductive learning methodology.
Adaptive and Selfadaptive Evolutionary Computations
 Computational Intelligence: A Dynamic Systems Perspective
, 1995
"... This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and selfadaptive parameter update rules currently in use ..."
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Cited by 82 (2 self)
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This paper reviews the various studies that have introduced adaptive and selfadaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and selfadaptive parameter update rules currently in use. Previous studies are reviewed and placed into a categorization that helps to illustrate their similarities and differences. Introduction
A Promising Genetic Algorithm Approach to JobShop Scheduling, Rescheduling, and OpenShop Scheduling Problems
 Proceedings of the Fifth International Conference on Genetic Algorithms
, 1993
"... The general jobshop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark jobshop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing con ..."
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Cited by 79 (2 self)
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The general jobshop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark jobshop scheduling problems, better than previous efforts using genetic algorithms for this task, and comparable to existing conventional searchbased methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in different parts of the genome. Our approach also promises to effectively address the openshop scheduling problem and the jobshop rescheduling problem. 1 INTRODUCTION The jobshop scheduling problem (JSSP) is a very important practical problem. Efficient methods of solving it can have major effects on profitability and product quality, but with the JSSP being amon...
An Overview of Genetic Algorithms: Part 1, Fundamentals
, 1993
"... this article may be reproduced for commercial purposes. 1 Introduction ..."
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Cited by 79 (1 self)
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this article may be reproduced for commercial purposes. 1 Introduction