A Fuzzy Genetic Algorithm Based on Binary Encoding for Solving Multidimensional Knapsack Problems

The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focuses on controlling the genetic parameters by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with other genetic operators, heuristics, and local search algorithms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

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A genetic algorithm rooted in integer encoding and fuzzy controller

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v8i2.pp113-124 ◽

2019 ◽

Vol 8 (2) ◽

pp. 113

Author(s):

M. Jalali Varnamkhasti

Keyword(s):

Genetic Diversity ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Search Algorithms ◽

Benchmark Problems ◽

Selection Mechanism ◽

Genetic Operators ◽

Local Search Algorithms ◽

Loss Of Genetic Diversity ◽

Essential Problem

The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.

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A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

Advances in Fuzzy Systems ◽

10.1155/2015/378156 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

S. M. Odeh ◽

A. M. Mora ◽

M. N. Moreno ◽

J. J. Merelo

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Fuzzy Logic Controller ◽

Decision Rules ◽

Great Influence ◽

Traffic Signal ◽

Signal System ◽

Fuzzy Genetic Algorithm ◽

Definition Of ◽

Fuzzy Genetic

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

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A Genetic Algorithm with Fuzzy Crossover Operator and Probability

Advances in Operations Research ◽

10.1155/2012/956498 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 16

Author(s):

Mohammad Jalali Varnamkhasti ◽

Lai Soon Lee ◽

Mohd Rizam Abu Bakar ◽

Wah June Leong

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Optimization Problems ◽

Performance Measure ◽

Population Diversity ◽

Crossover Operator ◽

Inference System ◽

Combinatorial Optimization Problems ◽

Fuzzy Genetic

The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probability selection technique is proposed based on the population diversity using a fuzzy logic controller. The measurement of the population diversity is based on the genotype and phenotype properties. In this fuzzy inference system, the selection of the crossover operator and its probability are controlled by a set of fuzzy rules derived from the fuzzy logic controller. Extensive computational experiments are conducted on the proposed algorithm, and the results are compared with some crossover operators commonly used for solving multidimensional 0/1 knapsack problems published in the literature. The results indicate that the proposed algorithm is effective in finding better quality solutions.

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