Book Chapter10.1007/978-3-540-92828-7_10
On Multi-Objective Evolutionary Algorithms
Dalila B.M.M. Fontes,António Gaspar-Cunha +1 more
- 01 Jan 2010
- pp 287-310
TL;DR: In this chapter Multi-Objective Evolutionary Algorithms (MOEAs) are introduced and some details discussed, with a brief discussion including their advantages and disadvantages, degree of applicability, and some known applications.
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Abstract: In this chapter Multi-Objective Evolutionary Algorithms (MOEAs) are introduced and some details discussed. A presentation of some of the concepts in which this type of algorithms are based on is given. Then, a summary of the main algorithms behind these approaches and their applications is provided, together with a brief discussion including their advantages and disadvantages, degree of applicability, and some known applications. Finally, future trends in this area and some possible paths for future research are pointed out.
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Citations
Improvement of Time and Frequency Domain Performance of Antipodal Vivaldi Antenna Using Multi-Objective Particle Swarm Optimization
TL;DR: The design of an antipodal Vivaldi antenna for ultra wideband (UWB) applications is presented, and multi-objective particle swarm optimization (MOPSO) is applied to handle these objectives simultaneously.
95
Reproducing fling-step and forward directivity at near source site using of multi-objective particle swarm optimization and multi taper
TL;DR: In this paper, a multi-objective evolutionary algorithm is used to minimize the differences between the response spectra and multi-tapered power spectral densities corresponding to the recorded and simulated waveforms.
17
Pareto-optimal front of cell formation problem in group technology
TL;DR: This paper designs the first exact branch-and-bound algorithm to create a Pareto-optimal front for the bi-criterion cell formation problem.
15
On a multiobjective optimal control of a tumor growth model with immune response and drug therapies
TL;DR: The multiobjective approach to the OC of the tumor growth model provides good-quality approximate solutions and has shown to be a valuable procedure to identify good trade-offs between conflicting objectives.
9
Optimization Methods for the Unit Commitment Problem in Electric Power Systems
Luís A. C. Roque
- 22 Oct 2014
TL;DR: The algorithm proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy, which has shown the proposed methodology to be an effective and effective combinatorial optimization method.
References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 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.
58.6K
•Book
Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
- 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.
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
•Book
Adaptation in natural and artificial systems
John H. Holland
- 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.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.