Evolutionary Computation

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Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation - Tập 8 Số 2 - Trang 125-147 - 2000
David A. Van Veldhuizen, Gary B. Lamont
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scient...... hiện toàn bộ
An Overview of Evolutionary Algorithms in Multiobjective Optimization
Evolutionary Computation - Tập 3 Số 1 - Trang 1-16 - 1995
Carlos M. Fonseca, P.J. Fleming
The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, tha...... hiện toàn bộ
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Evolutionary Computation - Tập 2 Số 3 - Trang 221-248 - 1994
Srinivas Nagaballi, Kalyanmoy Deb
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be intere...... hiện toàn bộ
Evolutionary Algorithms for Constrained Parameter Optimization Problems
Evolutionary Computation - Tập 4 Số 1 - Trang 1-32 - 1996
Zbigniew Michalewicz, Marc Schoenauer
Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been pr...... hiện toàn bộ
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation - Tập 8 Số 2 - Trang 173-195 - 2000
Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating thes...... hiện toàn bộ
Introducing Robustness in Multi-Objective Optimization
Evolutionary Computation - Tập 14 Số 4 - Trang 463-494 - 2006
Kalyanmoy Deb, Himanshu Gupta
In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbation...... hiện toàn bộ
A Hidden Markov Model Approach to the Problem of Heuristic Selection in Hyper-Heuristics with a Case Study in High School Timetabling Problems
Evolutionary Computation - Tập 25 Số 3 - Trang 473-501 - 2017
Ahmed Kheiri, Edward Keedwell
Abstract Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The h...... hiện toàn bộ
Empirical Investigation of Multiparent Recombination Operators in Evolution Strategies
Evolutionary Computation - Tập 5 Số 3 - Trang 347-365 - 1997
A. E. Eiben, Thomas Bäck
An extension of evolution strategies to multiparent recombination involving a variable number ϱ of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differing in their modality and separability and the regular/irregular arrangement of their local optima. Multiparent diagonal crossover and uniform scanning crossover...... hiện toàn bộ
Strongly Typed Genetic Programming
Evolutionary Computation - Tập 3 Số 2 - Trang 199-230 - 1995
David J. Montana
Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate ...... hiện toàn bộ
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation - Tập 9 Số 2 - Trang 159-195 - 2001
Nikolaus Hansen, Andreas Ostermeier
This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal ...... hiện toàn bộ
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