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What is Population Based Algorithm

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
An algorithm that maintains an entire set of candidate solutions, each solution corresponding to a unique point in the search space of the problem.
Published in Chapter:
Nature Inspired Methods for Multi-Objective Optimization
Sanjoy Das (Kansas State University, USA), Bijaya K. Panigrahi (Indian Institute of Technology, India), and Shyam S. Pattnaik (National Institute of Technical Teachers’ Training & Research, India)
DOI: 10.4018/978-1-60566-766-9.ch004
Abstract
This chapter focuses on the concepts of dominance and Pareto-optimality. It then addresses key issues in applying three basic classes of nature inspired algorithms – evolutionary algorithms, particle swarm optimization, and artificial immune systems, to multi-objective optimization problems. As case studies, the most significant multi-objective algorithm from each class is described in detail. Two of these, NSGA-II and MOPSO, are widely used in engineering optimization, while the others show excellent performances. As hybrid algorithms are becoming increasingly popular in optimization, this chapter includes a brief discussion of hybridization within a multi-objective framework.
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Multi-Objective Evolutionary Algorithms
An algorithm that maintains an entire set of candidate solutions, each solution corresponding to a unique point in the search space of the problem
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