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Journal ArticleDOI

Solution of constrained optimization problems by multi-objective genetic algorithm

TL;DR: In this article, a modified multi-objective algorithm is proposed for constrained optimization using the concept of Pareto dominance. And the algorithm effectively handles constraints encountered in both small-scale and large-scale optimization problems.
About: This article is published in Computers & Chemical Engineering.The article was published on 2002-10-15. It has received 73 citations till now. The article focuses on the topics: Population-based incremental learning & Meta-optimization.
Citations
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Journal ArticleDOI
TL;DR: A genetic algorithm-called BASIC, which is designed to take advantage of well known genetic schemes so as to be able to deal with numerous optimization problems, and provides an opportunity to the genetic operators to be extended with new schemes.

171 citations

Journal ArticleDOI
TL;DR: Using ANN-GA and SVR-GA strategies, a number of sets of optimized operating conditions leading to maximized yield and selectivity of the benzene isopropylation reaction product, namely cumene, were obtained.

106 citations

Journal ArticleDOI
TL;DR: In this article, a hybrid methodology comprising the Plackett-Burman (PB) design method, artificial neural networks (ANN) and genetic algorithms (GA) was utilized to determine optimum media composition and inoculum volume for the stated fermentative production of the exopolysaccharide (EPS).

83 citations

Journal ArticleDOI
TL;DR: Numerical results indicate that the presented approach can generate optimum solutions with higher accuracy when compared to Genetic algorithms (GAs), Particle Swarm Optimization (PSO) and GA hybrids with PSO (GAHPSO).

70 citations

Journal ArticleDOI
TL;DR: This article adapted the basic cross-entropy method for multi-objective optimisation and applied the proposed algorithm to known test problems, followed by an application to a dynamic, stochastic problem where a computer simulation model provides the objective function set.

70 citations

References
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Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

Journal ArticleDOI
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

41,772 citations

01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

33,034 citations

Book
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

32,573 citations

Book
01 Jan 1992
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Abstract: 1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

12,212 citations