• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.191-199
• /
• 2001

This paper presents a new genetic algorithm for job-shop scheduling problems. When we design a genetic algorithm for difficult ordering problems such as job-shop scheduling problems, it is important to design encoding/crossover that is excellent in characteristic preservation and to maintain a diversity of population. We used Job-based order crossover(JOX). Since the schedules generated by JOX are not always active-schedule, we proposed a method to transform them into active schedulesby using the GT method with c)laracteristic preservation. We introduce strategies for maintaining a diversity of the population by eliminating same individuals in the population. Furthermore, we are not used mutation. Experiments have been done on two examples: Fisher s and Thompson s $lO\timeslO and 20\times5$ benchmark problem.

• IE interfaces
• /
• v.14 no.3
• /
• pp.227-235
• /
• 2001

There is a rapidly growing demand for wireless telecommunication. However, the number of usable channel is very limited. Therefore, the problem of channel assignment becomes more and more important to use channels as efficiently as possible. The objective of this paper is to develop an evolution program (EP) to find an efficient dynamic channel assignment method for minimum interference among the channels within reasonable time. The series of specific channel number is used as a representation of chromosome. The only changed chromosomes by crossover and mutation are evaluated in each generation to save computation time and memory for the progress of improved EP. We can easily differentiate the fitness value of each chromosome using proposed evaluation function. We also control the weighting factor of the mutation rate and the used number of elitist chromosomes for the speed of convergence to the optimal solution.

• Advances in Computational Design
• /
• v.1 no.3
• /
• pp.275-296
• /
• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1177-1180
• /
• 1996

In this paper, we propose an algorithm that realizes cooperative behavior by construction of autonomous mobile robot system. Each robot is able to sense other robots and obstacles, and it has the rule of behavior to achieve the goal of the system. In this paper, to improve performance of the whole system, we use Genetic Programming based on Natural Selection. Genetic Programming's chromosome is a program of tree structure and it's major operators are crossover and mutation. We verify the effectiveness of the proposed scheme from the several examples.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.1025-1028
• /
• 2000

In this paper, we apply to a genetic algorithm for fuzzy clustering. We propose initialization procedure and genetic operators such as selection, crossover and mutation, which are suitable for solving the problems. To illustrate the effectiveness of the proposed algorithm, we solve the manufacturing cell formation problem and present computational comparisons to generalized Fuzzy c-Means algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.18-21
• /
• 1995

In this paper we describe a new method for multimodal function optimization using genetic algorithms(GAs). We propose adaptation rules for GA parameters such as population size, crossover probability and mutation probability. In the self organizing genetic algorithm(SOGA), SOGA parameters change according to the adaptation rules. Thus, we do not have to set the parameters manually. We discuss about SOGA and those of other approaches for adapting operator probabilities in GAs. The validity of the proposed algorithm will be verified in a simulation example of system identification.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.666-669
• /
• 2002

A microprocessor with genetic instructions such as crossover, mutation and inversion is proposed. The processor is modeled using VHDL, synthesized to a schematic and implemented on a FPGA. The control path is implemented with a microprogram consisting of about 15032-bit microwords, and the operation of each instruction is checked through simulation.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.17 no.3
• /
• pp.1-12
• /
• 1992

Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.33
• /
• pp.111-118
• /
• 1995

This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.5
• /
• pp.28-33
• /
• 1997

This paper presents an application of genetic algorithms for optimal configuration of distribution network. Optimal nehvork is defined to satisfy the condition of load balancing. Three problems are suggested to show the performance of genetic algorithms. To resolve the problems, we propose two different mutation operators, in stead of crossover and mutation operators, which are utilized in both global and local search operations. In addition, arc pattern list is also proposed for an efficient search.