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Recent Advances in Evolutionary Optimization in Noisy Environment—A Comprehensive Survey

  • Pratyusha RakshitEmail author
  • Amit Konar
Chapter
Part of the Cognitive Intelligence and Robotics book series (CIR)

Abstract

Noisy optimization is currently receiving increasing popularity for its widespread applications in engineering optimization problems, where the objective functions are often found to be contaminated with noisy sensory measurements. In the absence of knowledge of the noise statistics, discriminating better trial solutions from the rest becomes difficult in the “selection” step of an evolutionary optimization algorithm with noisy objective/s. This chapter provides a thorough survey of the present state-of-the-art research on noisy evolutionary algorithms for both single and multi-objective optimization problems. This is undertaken by incorporating one or more of the five strategies in traditional evolutionary algorithms. The strategies include (i) fitness sampling of individual trial solution, (ii) fitness estimation of noisy samples, (iii) dynamic population sizing over the generations, (iv) adaptation of the evolutionary search strategy, and (v) modification in the selection strategy.

Keywords

Evolutionary optimization Noise Uncertainty Sampling Population sizing Fitness estimation Selection 

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia

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