On the Use of Compact Approaches in Evolution Strategies

Abstract

Compact evolutionary algorithms have proven to be an efficient alternative for solving optimization problems in computing environments with low processing power. In this kind of solution, a probability distribution simulates the behavior of a population, thus looking for memory savings. Several compact algorithms have been proposed, including the compact genetic algorithm and compact differential evolution. This work aims to investigate the use of compact approaches in other important evolutionary algorithms: evolution strategies. This paper proposes two different approaches for compact versions of evolution strategies. Experiments were performed and the results analyzed. The results showed that, depending on the nature of problem, the use of the compact version of Evolution Strategies can be rewarding.

RETRACTED CHAPTER: Virtual Agent Societies to Provide Solutions to an Investment Problem

“…The platform proposed will be designed under the paradigm of Virtual Agent Societies to provide solutions to an investment problem in the business area, finding actions, behavior patterns and establishing more efficient investment portfolio programs. Virtual Agents Organizations have been identified as the most suitable technology for automatic knowledge management [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], due to their robustness and autonomy. VAOs establish the level of abstraction needed to communicate with the environment and interact with other VAOs cooperatively for problem solving [].…”

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…In the literature, there are examples of compact Evolution Strategy (cES) algorithms, such as c(1 + 1)-ES and the c(μ, λ)-ES [280]. From the experimental analysis in [280], the c(1 + 1)-ES algorithm appeared to be as efective as the original (1 + 1)-ES.…”

“…In the literature, there are examples of compact Evolution Strategy (cES) algorithms, such as c(1 + 1)-ES and the c(μ, λ)-ES [280]. From the experimental analysis in [280], the c(1 + 1)-ES algorithm appeared to be as efective as the original (1 + 1)-ES. Conversely, c(μ, λ)-ES appeared to perform worse than its population-based (μ, λ)-ES counterpart, especially for high values of λ.…”

A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments

“…-Compact variants of Evolutionary Algorithms (cEAs): the compact Genetic Algorithm with real encoding (rcGA) (Mininno et al, 2008), compact Differential Evolution (cDE) (Mininno et al, 2011), compact Evolutionary Strategies (Sergio et al, 2014) and Covariance Matrix compact Differential Evolution (CMcDE) (Jewajinda, 2016);…”