From the computational point of view, the job shop scheduling problem (JSP) is one of the
most notoriously intractable NP-hard optimization problems. This paper applies an effective …

Abstract Genetic Algorithms (GAs) are effective approximation algorithms which focus on
“hopeful area” in searching process. However, in harder problems, it is often very difficult to …

In this paper, we propose a new Genetic Algorithm for JSP using two crossovers. The
crossover, JOX, obtained relatively good results, however offspring generated by JOX exist …

The deterministic Multi-step Crossover Fusion (dMSXF) is an improved crossover method of
MSXF which is a promising method of JSP, and it shows high availability in TSP. Both of …

Multi-step Crossover Fusion (MSXF) and deterministic MSXF (dMSXF) are promising
crossover operators that perform multi-step neighborhood search between parents, and …

抄録 Job-shop Scheduling Problem (JSP) is one of the most difficult benchmark problems.
GA approaches often fail searching the global optimum because of the deception UV …

Genetic algorithm (GA) is an effective method of solving combinatorial optimization
problems. Generally speaking most of search algorithms require a large execution time in …

The utilization rate of products, which indicates the extent to which product functionality has
been exhausted, must be improved to increase eco-efficiency of the products. In this paper …

Niching ethods are useful extension of evolutionary algorithm that can permit population to
search many peaks in parallel in multimodal domains. Traditional niching methods that can …

抄録 We propose an genetic algorithm (GA) that applies to the traveling salesman problem
(TSP). The GA uses edge assembly crossover (EAX), which is known to be effective for …