Open AccessAn Adaptive Approach to Controlling Parameters and Population Size of Evolutionary Algorithm
Author(s) -
Syafiul Muzid
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1430/1/012048
Subject(s) - evolutionary algorithm , mathematical optimization , population , travelling salesman problem , evolutionary computation , fuzzy logic , algorithm , evolutionary programming , cultural algorithm , computer science , population size , mathematics , genetic algorithm , mutation , 2 opt , convergence (economics) , population based incremental learning , artificial intelligence , biochemistry , chemistry , demography , sociology , economic growth , economics , gene
Evolutionary algorithm (EA) is a part of evolutionary computing inspired by the theory of biological evolution. Evolutionary algorithms have three parameters that must be defined, namely population size, probability of crossing over, and probability of mutation. The absence of standard rules in setting the value of these parameters becomes a difficulty in utilizing evolutionary algorithms to solve optimization problems so that it can cause early convergence in obtaining local optimum values. This research was conducted to find a way to adjust the value of these parameters by using fuzzy logic to determine the probability of crossing and mutation probability and the method of determining population size based on the best fitness value. This method is called Population Resizing on Fitness Improvement Fuzzy Evolutionary Algorithm (PRoFIFEA). The testing of this method uses the problem of Traveling Salesman Problem (TSP) compared to the standard evolutionary algorithm method which states that the PRoFIFEA method is able to find a more optimal solution.