Infeasibility Driven Evolutionary Algorithm with ARIMA-Based Prediction Mechanism

Filipiak, Patryk; Michalak, Krzysztof; Lipiński, Piotr

doi:10.1007/978-3-642-23878-9_41

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Tapabrata Ray maintained a small percentage of infeasible solutions close to constraint boundaries during its course of evolution for constrained optimization (Ray, 2009). Patryk Filipiak and Maristela OliveiraSantos separately proposed an infeasibility-driven evolutionary algorithm and infeasibilityhandling approach in genetic algorithm (Filipiak, 2011;Santos, 2010). Both techniques use infeasible solutions in evolutionary computation.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Computation Based on Immune Operation for Constraint Optimization Problems

Zhang¹,

Ogura²,

Ma³

et al. 2016

Rev. Téc. Ing. Univ. Zulia.

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A large number of infeasible solutions often occur in population of evolutionary Computation (EC) solving the constraint combinatorial optimization problems. The greater the number of infeasible solutions in the population, the worse the performance of ECto search the solution, in the worst case, the algorithm ceases to run. The existing methods, penalty function or multi-objective optimization, can relieve partly the worst case of EC to run. However, they are actually to restrain the infeasible solutions surviving in population, the performance of the EC is not improved. In this study we propose an approach using an important feature of the infeasible solutions in Genetic Algorithms (GA). The approach can not only solve the problem of algorithm ceases to run, but also improve effectively the performance of genetic algorithmssearching the optimal solution.From examination of the proposed method on multidimensional knapsack problems, the application of method is effective tosolve the problem of algorithm ceases to run as well as to improve clearly the performance of GA.

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Section: Introductionmentioning

confidence: 99%

An Evolutionary Computation Based on Immune Operation for Constraint Optimization Problems

Zhang¹,

Ogura²,

Ma³

et al. 2016

Rev. Téc. Ing. Univ. Zulia.

View full text Add to dashboard Cite

show abstract

“…Tapabrata Ray [7] (2009) maintained a small percentage of infeasible solutions close to constraint boundaries during its course of evolution for constrained optimization. Patryk Filipiak [8] (2011) and Maristela OliveiraSantos [9] (2010) separately proposed an infeasibility-driven evolutionary algorithm and infeasibility-handling approach in genetic algorithm (GA). Both techniques use infeasible solutions in evolutionary computation.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Using Infeasible Solutions for Constrained Optimization Problems

Zhang¹,

Ogura²,

Ma³

et al. 2014

TOCSJ

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The use of genetic algorithms (GAs) to solve combinatorial optimization problems often produces a population of infeasible solutions because of optimization problem constraints. A solution pool with a large number of infeasible solutions results in poor search performance of a GA, or worse, the algorithm ceases to run. In such cases, the methods of penalty function and multi-objective optimization can help GAs run to some extent. However, these methods prevent infeasible solutions from surviving in the solutions pool. Infeasible solutions, particularly those that are produced after several generations, exhibit some achievements in evolutionary computation. They should serve as a positive function in the process of evolution instead of being abandoned from the solution pool. In this study, we extract excellent gene segment for infeasible solutions with a function operation to increase the search performance of GAs. Simulation results on zero-one knapsack problems demonstrate that applying infeasible solutions can improve the search capability of GAs.

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Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems

Filipiak

Lipiński

2015

Applications of Evolutionary Computation

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Infeasibility Driven Evolutionary Algorithm with ARIMA-Based Prediction Mechanism

Cited by 7 publications

References 6 publications

An Evolutionary Computation Based on Immune Operation for Constraint Optimization Problems

An Evolutionary Computation Based on Immune Operation for Constraint Optimization Problems

A Genetic Algorithm Using Infeasible Solutions for Constrained Optimization Problems

Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems

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