Add to ORKGIn this paper we optimize run-time performance of the genetic algorithm by caching. We are caching the genetic algorithm procedure for evaluation of an objective function. Least Recently Used (LRU) caching strategy is used, that is simple but effective. This approach is good for problems that have a relatively small length of item string, and a large evaluation time of objective function. We present results of the caching to genetic algorithm for solving one such problem - the simple plant location problem (SPLP)
Genetic algorithms (aka GA's) are a robust global search strategy that ignore local minima and irrel...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
Abstract: The use of genetic algorithms (GAs) to solve combinatorial optimization problems often pro...
This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure ...
The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solv...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This paper proposes a novel methodology for cache replacement policy based on techniques of genetic ...
A genetic algorithm approach suitable for solving multi-objective optimization problems is described...
peer reviewedAchieving a balance between the exploration and exploitation capabilities of genetic al...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
Abstract. Crnctic algorithms have been applied to many oplirnization and search problems and shown t...
Traditional search methods have always suffered from both spatial and temporal expansion, especiall...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Abstract. Satisfiability testing (SAT) is a very active area of research today, with numerous real-w...
In this paper, an improved genetic algorithm (GA) for solving the multi-level uncapacitated facility...
Genetic algorithms (aka GA's) are a robust global search strategy that ignore local minima and irrel...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
Abstract: The use of genetic algorithms (GAs) to solve combinatorial optimization problems often pro...
This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure ...
The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solv...
The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete f...
This paper proposes a novel methodology for cache replacement policy based on techniques of genetic ...
A genetic algorithm approach suitable for solving multi-objective optimization problems is described...
peer reviewedAchieving a balance between the exploration and exploitation capabilities of genetic al...
Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a...
Abstract. Crnctic algorithms have been applied to many oplirnization and search problems and shown t...
Traditional search methods have always suffered from both spatial and temporal expansion, especiall...
Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]...
Abstract. Satisfiability testing (SAT) is a very active area of research today, with numerous real-w...
In this paper, an improved genetic algorithm (GA) for solving the multi-level uncapacitated facility...
Genetic algorithms (aka GA's) are a robust global search strategy that ignore local minima and irrel...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
Abstract: The use of genetic algorithms (GAs) to solve combinatorial optimization problems often pro...