Unraveling Nature's Evolutionary Optimization Strategic Algorithms

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 46 - 61

Published: May 14, 2024

Evolutionary algorithms are inspired by Darwinian evolution mimicking the mechanisms of natural selection. The most well-known type, namely genetic (GAs), uses populations potential solutions represented as chromosomes, subjecting them to selection, crossover, and mutation operations. Tailored for specific problems characteristics, they tend be today's much murmured research. This chapter proposes different EAs their systematic workflow. EA, process, begins with initialization a population solutions. These undergo evaluation based on predefined fitness function. Crossover operations then generate new candidate iterative process continues until convergence or stopping criterion is met. performance EA depends parameter settings. Tuning parameters crossover rates, size. EA's have been rooted in elegance nature's optimization strategies. They evolved into indispensable tools solving complex across domains. It has changed valuable asset many researchers. overall perspective various ways discussed chapter.

Language: Английский

QoS-SLA-aware Optimization Framework for IoT-Service Placement in Integrated Fog-Cloud Computing

Journal of Grid Computing, Journal Year: 2024, Volume and Issue: 23(1)

Published: Dec. 18, 2024

Language: Английский