Most real-world optimization problems consist of linear cost functions subject to a set of constraints. In genetic algorithms the techniques for coping with such constraints are manifold: penalty functions, keeping the population in the feasible region, etc. Mutation and crossover operators must take into account the specific features of this kind of problems, as they are the responsible of the...

This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are conflicting. In order to account f...

Ordinarily, Genetic Programming uses little or no mutation. Crossover is the predominant operator. This study tests the eeect of a very aggressive use of the mutation operator on the generalization performance of our Compiling Genetic Programming System ('CPGS'). We ran our tests on two benchmark classiication problems on very sparse training sets. In all, we performed 240 complete runs of popu...

A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action. We then consider the problem of characterizing crossove...

In this paper, for the problem of high total fuel consumption distribution trucks when multiple concrete-mixing plants distribute concrete together, we established a green model and solved with an improved genetic algorithm to obtain scheme trucks. Firstly, is characteristics commercial tankers; secondly, adaptive elite retention strategy, crossover, mutation operator, immune operation are adde...

Genetic algorithms (GAs) are multi-dimensional, blind heuristic search methods that involve complex interactions among parameters (such as population size, number of generations, GA operators and operator probabilities). The question whether the quality of results obtained by GAs depend upon the values given to these parameters, is a matter of research interest. This work studies the problem of...

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation o...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA d...

In this paper we present the outcome of two recent sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair. This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the genetic algorithm. Following implementation and testing of our genetic algotihm with GeneRepair we fou...