Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems

Abstract

In this paper, we extend the Compact Genetic Algorithm (CGA) for real-valued optimization problems by dividing the total search process into three stages. In the first stage, an initial vector of probabilities is generated. The initial vector contains the probabilities of bits having 1 depending on the bit locations as defined in the IEEE-754 standard. In the second stage, a CGA search is applied on the objective function using the same encoding scheme. In the last stage, a local search is applied using the result obtained by the previous stage as the starting point. A simulation study is performed on a set of well-known test functions to measure the performance differences. Simulation results show that the improvement in search capabilities is significant for many test functions in many dimensions and different levels of difficulty.

Metaheuristics: A Julia Package for Single- and Multi-Objective Optimization

“…Implemented metaheuristic algorithms are detailed in Table 1. Algorithms for single-objective optimization include Evolutionary Centers Algorithm (ECA) (Mejía-de-Dios & Mezura-Montes, 2019), Differential Evolution (DE) (Price, 2013), Particle Swarm Optimization (PSO) (Kennedy & Eberhart, 1995), Artificial Bee Colony (ABC) (Karaboga & Basturk, 2007), Gravitational Search Algorithm (GSA) (Mirjalili & Gandomi, 2017), Simulated Annealing (SA) (Van L. & Aarts, 1987), Whale Optimization Algorithm (WOA) (Mirjalili & Lewis, 2016), and Machine Coded Compact Genetic Algorithms (MCCGA) (Satman & Akadal, 2020). Metaheuristics also includes multi-objective optimization algorithms such as a Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D-DE) (Li & Zhang, 2008), Non-dominated Sorting Genetic Algorithms (NSGA-II,-III) (Deb et al, 2002;Deb & Jain, 2014), 𝑆-Metric Selection Evolutionary Multi-objective Algorithm (SMS-EMOA) (Emmerich et al, 2005), Improved Strength Pareto Evolutionary Algorithm (SPEA2) (Eckart Zitzler et al, 2001)m and Coevolutionary Framework for Constrained Multiobjective Optimization (CCMO) (Tian et al, 2021).…”

A Novel Automatic Relational Database Normalization Method

“…Many generations are created until a convergence criterion is met. The number of individuals in the population, the number of generations and the probability of applying mating operators determine the performance of a GA (Goldberg, 1989;Lim et al, 2017;Satman & Akadal, 2020).…”