Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm

Dozier, Gerry; Bowen, James; Bahler, Dennis

doi:10.1109/icec.1994.349934

Cited by 55 publications

(39 citation statements)

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“…He reports faster and better results with the µGA on two simple stationary functions and on a real-world, engineering control problem. µGAs have also been applied to the optimization of an air-injected hydrocyclone (Karr, 1991a), to the design of fuzzy logic controllers (Karr, 1991b), to the solution of the k-queens problem (Dozier et al, 1994) and to the optimization of chemical oxygeniodine laser (Carrol, 1996).…”

Section: Introductionmentioning

confidence: 99%

Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

Karim

Suzuki

Kai

1970

J. nav. arch. mar. engg.

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Section: Introductionmentioning

confidence: 99%

Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

Karim

Suzuki

Kai

1970

J. nav. arch. mar. engg.

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“…Dozier et al in [9] presented two heuristic-based micro genetic algorithms which quickly find solutions to constraints satisfaction problem. They experimented with different sizes of micro population and found that for a particular problem, a relatively small number of individuals in the genetic algorithm was sufficient.…”

Section: A Previous Workmentioning

confidence: 99%

A Micro Artificial Immune System

Lozada¹,

Calvo²,

Taud³

2011

Polibits

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Abstract-In this paper, we present a new algorithm, namely, a micro artificial immune system (Micro-AIS) based on the Clonal Selection Theory for solving numerical optimization problems. For our study, we consider the algorithm CLONALG, a widely used artificial immune system. During the process of cloning, CLONALG greatly increases the size of its population. We propose a version with reduced population. Our hypothesis is that reducing the number of individuals in a population will decrease the number of evaluations of the objective function, increasing the speed of convergence and reducing the use of data memory. Our proposal uses a population of 5 individuals (antibodies), from which only 15 clones are obtained. In the maturation stage of the clones, two simple and fast mutation operators are used in a nominal convergence that works together with a reinitialization process to preserve the diversity. To validate our algorithm, we use a set of test functions taken from the specialized literature to compare our approach with the standard version of CLONALG. The same method can be applied in many other problems, for example, in text processing.

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“…Roughly speaking, these EAs can be divided into two categories: those based on exploiting heuristic information on the constraint network [6,14,21,22], and those using a fitness function (penalty function) that is adapted during the search [2,4,5,7,9,10,17,18]. In this paper we investigate three methods from the second category: the co-evolutionary method by Paredis [17], the heuristic-based microgenetic algorithm by Dozier et al [4], and the EA with stepwise adaptation of weights by Eiben et al [10]. We implement three specific evolutionary algorithms based on the corresponding methods, called COE, SAW, and MID, respectively, and compare them on a test suite consisting of randomly generated binary CSPs with finite domains.…”

Section: Introductionmentioning

confidence: 99%

Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function

Eiben

Hemert

Marchiori

et al. 1998

Lecture Notes in Computer Science

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Abstract. This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approach are implemented. For highly connected constraint networks, the results provide further empirical support to the theoretical prediction of the phase transition in binary CSPs. IntroductionEvolutionary algorithms are usually considered to be ill-suited for solving constraint satisfaction problems. Namely, the traditional search operators (mutation and recombination) are 'blind' to the constraints, that is, parents satisfying a certain constraint may very well result in an offspring that violates it. Furthermore, while EAs have a 'basic instinct' to optimize, there is no objective function in a CSP -just a set of constraints to be satisfied. Despite such general arguments, in the last years there have been reports on quite a few EAs for solving CSPs having a satisfactory performance. Roughly speaking, these EAs can be divided into two categories: those based on exploiting heuristic information on the constraint network [6,14,21,22], and those using a fitness function (penalty function) that is adapted during the search [2,4,5,7,9,10,17,18]. In this paper we investigate three methods from the second category: the co-evolutionary method by Paredis [17], the heuristic-based microgenetic algorithm by Dozier et al [4], and the EA with stepwise adaptation of weights by Eiben et al. [10]. We implement three specific evolutionary algorithms based on the corresponding methods, called COE, SAW, and MID, respectively, and compare them on a test suite consisting of randomly generated binary CSPs with finite domains. The results of the experiments are used to assess empirically the relative performance of the three different methods within the same category, thereby providing suggestions as to which implementation of the same general idea is the most promising. We use randomly generated problem instances for the experiments, where the hardness of the problem instances is influenced by two parameters: constraint density and constraint tightness. By running experiments on 25 different combinations of these parameters we gain detailed feedback on EA behavior and can validate theoretical predictions on the location of the

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Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm

Cited by 55 publications

References 4 publications

Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

Optimal design of hydrofoil and marine propeller using micro-genetic algorithm (Î¼GA)

A Micro Artificial Immune System

Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function

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