Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems-Comparative Study

Abstract

Abstract-Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs). EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI). EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm (RCGA), elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.

Concepts Selection in Fuzzy Cognitive Map using Evolutionary Learning Algorithm based on Graph Theory Metrics

“…In this paper Elite Genetic Algorithm [14] and Individually Directed Evolutionary Algorithm were used [11]. The genetic operators were applied only to the W ′ vector.…”

“…The comparison of the developed approach with the standard one based on the all possible concepts and data error and the previously developed approach based on density and system performance indicators [12] was done. The learning process was performed using two effective techniques for FCMs learning: Elite Genetic Algorithm (EGA) [14] and Individually Directional Evolutionary Algorithm (IDEA) [11].…”

An Economic Decision Support System based on Fuzzy Cognitive Maps with Evolutionary Learning Algorithm

“…where p j,i takes value in the range of [−1, 1]. 5) The total influence between concepts can be used to determine other system performance indicators, for example the impact of the j-th concept on the system (9) and the impact of the system on the i-th concept (10):…”

“…Use evolutionary algorithm to generate new population. In the simulation analysis of the proposed approach Elite Genetic Algorithm and Individually Directed Evolutionary Algorithm were used [10].…”

“…The comparison of the developed approach with the standard one based on data error and the approach based on density was done. The learning process was accomplished with the use of Elite Genetic Algorithm (EGA) and Individually Directional Evolutionary Algorithm (IDEA) [10].…”