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27
An Overview of Genetic Algorithms: Part 1, Fundamentals
, 1993
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Cited by 83 (1 self)
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this article may be reproduced for commercial purposes. 1 Introduction
Realcoded Genetic Algorithms, Virtual Alphabets, and Blocking
 Complex Systems
, 1990
"... This paper presents a theory of convergence for realcoded genetic algorithmsGAs that use floatingpoint or other highcardinality codings in their chromosomes. The theory is consistent with the theory of schemata and postulates that selection dominates early GA performance and restricts subseque ..."
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Cited by 79 (7 self)
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This paper presents a theory of convergence for realcoded genetic algorithmsGAs that use floatingpoint or other highcardinality codings in their chromosomes. The theory is consistent with the theory of schemata and postulates that selection dominates early GA performance and restricts subsequent search to intervals with aboveaverage function value, dimension by dimension. These intervals may be further subdivided on the basis of their attraction under genetic hillclimbing. Each of these subintervals is called a virtual character, and the collection of characters along a given dimension is called a virtual alphabet. It is the virtual alphabet that is searched during the recombinative phase of the genetic algorithm, and in many problems this is sufficient to ensure that good solutions are found. Although the theory helps suggest why many problems have been solved using realcoded GAs, it also suggests that realcoded GAs can be blocked from further progress in those situations whe...
A Genetic Approach to the Quadratic Assignment Problem
, 1995
"... The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algori ..."
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Cited by 55 (7 self)
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The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algorithm (GA) approach to QAP. Genetic algorithms are a class of randomized parallel search heuristics which emulate biological natural selection on a population of feasible solutions. We present computational results which show that this GA approach finds solutions competitive with those of the best previouslyknown heuristics, and argue that genetic algorithms provide a particularly robust method for QAP and its more complex extensions. 5 A Genetic Approach to the Quadratic Assignment Problem David M. Tate and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 4126249837 4126249831 (Fax) 1. Introduction The Quadrat...
A hierarchy of evolution programs: An experimental study
 Evolutionary Computation
, 1993
"... zbyszek�mosaic.uncc.edu In this paper we present the concept of evolution programs and discuss a hier� archy of such programs for a particular problem. We argue that �for a particular problem � stronger evolution programs �in terms of the problem�speci�c knowledge incorporated in the system � should ..."
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Cited by 19 (4 self)
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zbyszek�mosaic.uncc.edu In this paper we present the concept of evolution programs and discuss a hier� archy of such programs for a particular problem. We argue that �for a particular problem � stronger evolution programs �in terms of the problem�speci�c knowledge incorporated in the system � should perform better than weaker ones. This hypothe� sis is based on a number of experiments and a simple intuition that problem�speci�c knowledge enhances an algorithm in terms of its performance � at the same time it narrows the applicability of an algorithm. Trade�o�s between the e�ort of �nding an e�ective representation for general�purpose evolution programs and the e�ort of developing more specialized systems are also discussed. 1
Using Neural Networks and Genetic Algorithms as Heuristics for NPComplete Problems
, 1983
"... Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NPComplete, any other NPComplete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either parad ..."
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Cited by 15 (8 self)
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Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NPComplete, any other NPComplete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. INTRODUCTION NPComplete problems are problems that are not currently solvable in polynomial time. However, they are polynomially equivalent in the sense that any NPComplete problem can be transformed into any other in polynomial time. Thus, if any NPComplete problem can be solved in polynomial time, they all can [Garey]. The canonical example of an NPComplete problem is the boolean satisfiability (SAT) problem: Given an arbitrary boolean expression of n variables, does there exist an assignment to those variables such that the expression is true? Other familiar examples include job shop scheduling, bin packing, a...
Blending Heuristics with a PopulationBased Approach: A "Memetic" Algorithm for the Traveling Salesman Problem
 REPORT 9212, UNIVERSIDAD NACIONAL DE LA PLATA, C.C. 75, 1900 LA PLATA
, 1994
"... Very recently many researchers, with backgrounds in parallel computing, started to develop hybrids of traditional genetic algorithms. The main departure from standard genetic algorithms is that these new methods incorporate specific heuristics for the problem at hand (drawing on a tradition which ha ..."
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Cited by 10 (4 self)
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Very recently many researchers, with backgrounds in parallel computing, started to develop hybrids of traditional genetic algorithms. The main departure from standard genetic algorithms is that these new methods incorporate specific heuristics for the problem at hand (drawing on a tradition which has roots outside the genetic framework) and which we apply within a stochastic game that exerts a selective pressure. The heuristics are used for periods of individual optimization, that is when agents do not interact. New computational results for the Traveling Salesman Problem will be presented in this paper. The approach is prepared to include Tabu Search techniques, introducing a new crossover operator (which is called Random Respectful Corner Recombination) and a special pair of a topology and set of rules for the interaction between agents. The approach has a natural parallelism and a feature called superlinear speedup will also be discussed.
Genetic Algorithms and Network Ring Design
, 1999
"... Optimal network ring design is a difficult problem characterised by the requirement to compare a large number of potential solutions (network designs). The problem of network ring design can be described as consisting of three parts: routing, link capacity assignment and ring determination. It has t ..."
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Cited by 9 (0 self)
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Optimal network ring design is a difficult problem characterised by the requirement to compare a large number of potential solutions (network designs). The problem of network ring design can be described as consisting of three parts: routing, link capacity assignment and ring determination. It has traditionally been broken down into a number of subproblems, solved in sequence, and usually by heuristics thereby leading to locallyoptimal design solutions. Genetic Algorithms (GAs) have shown themselves to be efficient at searching large problem spaces and have been successfully used in a number of engineering problem areas, including telecommunications network design. We present an approach of a GA to the network ring design problem in which the GA representation encapsulates all aspects of the problem and solves them simultaneously. A novel, hybrid bit and permutation representation is described along with the fitness function for the design problem. Results of applying this representat...
Adaptive Reconfiguration of Data Networks Using Genetic Algorithms
, 2002
"... Genetic algorithms are applied to an important, but littleinvestigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traf ..."
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Cited by 8 (0 self)
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Genetic algorithms are applied to an important, but littleinvestigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traffic patterns; and the quality of service (QoS) requirements and priorities of different users and applications. Dynamic reconfiguration is possible in networks that contain links whose endpoints can be easily changed, such as satellite channels or terrestrial wireless connections. We report results that demonstrate the feasibility of performing genetic search quickly enough for online adaptation.
Genetic Algorithms in Economics and Finance: Forecasting Stock . . .
 GENETIC ALGORITHMS AND GENETIC PROGRAMMING IN COMPUTATIONAL FINANCE, DARDRECHT
, 1998
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