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38
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...
Evolving Networks: Using the Genetic Algorithm with Connectionist Learning
 In
, 1990
"... It is appealing to consider hybrids of neuralnetwork learning algorithms with evolutionary search procedures, simply because Nature has so successfully done so. In fact, computational models of learning and evolution offer theoretical biology new tools for addressing questions about Nature that hav ..."
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Cited by 179 (2 self)
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It is appealing to consider hybrids of neuralnetwork learning algorithms with evolutionary search procedures, simply because Nature has so successfully done so. In fact, computational models of learning and evolution offer theoretical biology new tools for addressing questions about Nature that have dogged that field since Darwin [Belew, 1990]. The concern of this paper, however, is strictly artificial: Can hybrids of connectionist learning algorithms and genetic algorithms produce more efficient and effective algorithms than either technique applied in isolation? The paper begins with a survey of recent work (by us and others) that combines Holland's Genetic Algorithm (GA) with connectionist techniques and delineates some of the basic design problems these hybrids share. This analysis suggests the dangers of overly literal representations of the network on the genome (e.g., encoding each weight explicitly). A preliminary set of experiments that use the GA to find unusual but successf...
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
A Learning Approach to Personalized Information Filtering
, 1994
"... A personalized information filtering system must specialize to current interests of the user and adapt as they change over time. It must also explore newer domains for potentially interesting information. A learning approach to building personalized information filtering systems is proposed. The sys ..."
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Cited by 76 (0 self)
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A personalized information filtering system must specialize to current interests of the user and adapt as they change over time. It must also explore newer domains for potentially interesting information. A learning approach to building personalized information filtering systems is proposed. The system is designed as a collection of information filtering interface agents. Interface Agents are intelligent and autonomous computer programs which learn users' preferences and act on their behalf  electronic personal assistants that automate tasks for the user. This thesis presents the basic framework for personalized information filtering agents, and describes an implementation, "Newt", built using the framework. Newt uses a keyword based filtering algorithm. The learning mechanisms used are relevance feedback and the genetic algorithm. The user interface is friendly and accessible to both naive as well as power users. Experimental
Vehicle Routing with Time Windows using Genetic Algorithms
, 1995
"... In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the cost of servicing the set of customers without being ..."
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Cited by 38 (3 self)
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In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. As finding a feasible solution to the problem is NPcomplete, search methods based upon heuristics are most promising for problems of practical size. In this paper we describe GIDEON, a genetic algorithm heuristic for solving the VRPTW. GIDEON consists of a global customer clustering method and a local postoptimization method. The global customer clustering method uses an adaptive search strategy based upon population genetics, to assign vehicles to customers. The best solution obtained from the clustering method is improved by a local postoptimization method. The synergy a between global adaptive clustering method and a local route optimization method produce better results than those obtained by competing heuristic search methods. On a standard set of 56 VRPTW problems obtained from the literature the GIDEON system obtained 41 new best known solutions.
Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Methods for Vehicle Routing Problems with Time Windows. Working paper
, 1993
"... The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all customers while minimizing the number of veh ..."
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Cited by 32 (1 self)
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The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all customers while minimizing the number of vehicles and travel distance without violating the capacity and travel time of the vehicles and customer time constraints. In this paper we describe a λinterchange mechanism that moves customers between routes to generate neighborhood solutions for the VRPTW. The λinterchange neighborhood is searched using Simulated Annealing and Tabu Search strategies. The initial solutions to the VRPTW are obtained using the PushForward Insertion heuristic and a Genetic Algorithm based sectoring heuristic. The hybrid combination of the implemented heuristics, collectively known as the GenSAT system, were used to solve 60 problems from the literature with customer sizes varying from 100 to 417 customers. The computational results of GenSAT obtained new best solutions for 40 test problems. For the remaining 20 test problems, 11 solutions obtained by the GenSAT system equal previously known best solutions. The average performance of GenSAT is significantly better than known competing heuristics. For known optimal solutions to the VRPTW problems, the GenSAT system obtained the optimal number of vehicles. Keywords:
Breeding hybrid strategies: Optimal behaviour for oligopolists
 Journal of Evolutionary Economics
, 1992
"... Abstract. Oligopolistic pricing decisions in which the choice variable is not dichotomous as in the simple prisoner's dilemma but continuous have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effec ..."
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Cited by 32 (9 self)
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Abstract. Oligopolistic pricing decisions in which the choice variable is not dichotomous as in the simple prisoner's dilemma but continuous have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoport's Tit for Tat for the threeperson repeated game. Holland's genetic algorithm and Axelrod's representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions. The paper discusses how findings from twoperson tournaments can be extended to the GPD, in particular how the author's winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of "niche " strategies, successful against a particular environment of competitors. Bootstrapping, or breeding strategies against their peers, provides a means of
Evolutionary algorithms in control system engineering: a survey. Control Engineering Practice
 Control Engineering Practice, Vol
, 2002
"... Abstract: Developments in computational models of evolutionary processes have led to the realisation of powerful, robust and general optimization and adaptive systems collectively called evolutionary algorithms. In this paper we provide an overview of evolutionary algorithms and consider the feature ..."
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Cited by 31 (1 self)
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Abstract: Developments in computational models of evolutionary processes have led to the realisation of powerful, robust and general optimization and adaptive systems collectively called evolutionary algorithms. In this paper we provide an overview of evolutionary algorithms and consider the features and characteristics that are particularly appropriate for control engineering applications. The versatile and robust qualities of these algorithms are considered and a number of application areas described.
A User's Guide to GAucsd 1.4
, 1992
"... This document describes the GAucsd system for function optimization based on genetic search techniques. Genetic algorithms appear to hold a lot of promise as general purpose adaptive search procedures. However, the authors disclaim any warranties of fitness for a particular problem. The purpose of m ..."
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Cited by 30 (0 self)
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This document describes the GAucsd system for function optimization based on genetic search techniques. Genetic algorithms appear to hold a lot of promise as general purpose adaptive search procedures. However, the authors disclaim any warranties of fitness for a particular problem. The purpose of making this system available is to encourage the experimental use of genetic algorithms on realistic optimization problems, and thereby to identify the strengths and weaknesses of genetic algorithms. GAucsd was developed by Nicol Schraudolph at the University of California, San Diego; it is based on Genesis 4.5, a genetic algorithm package written by John J. Grefenstette. GAucsd and related materials are available via anonymous ftp from cs.ucsd.edu (132.239.51.3) in the pub/GAucsd directory or via electronic mail from the first author, who welcomes bug reports, comments and suggestions, and maintains a mailing list of users to announce patches and new releases. Hardcopies of this documen...
Adaptation in Constant Utility NonStationary Environments
 In Proceedings of the Fourth International Conference on Genetic Algorithms
, 1995
"... Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of effective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a broad class of nonstationary environments, those which combi ..."
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Cited by 17 (2 self)
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Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of effective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a broad class of nonstationary environments, those which combine a variable result function with an invariant utility function, and demonstrate via simulation that an adaptive strategy employing both evolution and learning can tolerate a much higher rate of environmental variation than an evolutiononly strategy. We suggest that in many cases where stability has previously been assumed, the constant utility nonstationary environment may in fact be a more powerful viewpoint. 1 Nonstationary environments An adaptive system within an environment performs two basic tasks. First, there is the search for, and the representation of, regularities in the history of interactions with the environment. Second, there is the attempt to gain some advantage from th...