The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first investigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operat...

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are...

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

Gradient descent techniques such as back propagation have been used effectively to train neural network connection weights; however, in some applications gradient information may not be available. Biologically inspired genetic algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connection weights demonstrated that exchanging genetic material betwe...

The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio co...

We study the benefits of Genetic Algorithms, in particular the crossover operator, in constructing experimental designs that are D-optimal. To this purpose, we use standard Monte Carlo algorithms such as DETMAX and k-exchange as the mutation operator in a Genetic Algorithm. Compared to the heuristics, our algorithms are slower but yield better results. Key-Words: Genetic Algorithm, Memetic Algo...

An important aspect of heterogeneous computing systems is the problem of efficiently mapping tasks to processors. There are various methods of obtaining acceptable solutions to this problem but the genetic algorithm is considered to be among the best heuristics for assigning independent tasks to processors. This paper focuses on how the genetic heuristic can be improved by determining the best ...

The traditional concept of a genetic algorithm (GA) is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems we decided to explore this by comparing two tes...

This paper assumes a search space of fixed-length strings, where the size of the alphabet can vary from position to position. Structural crossover is mask-based crossover, and thus includes n-point and uniform crossover. Structural mutation is mutation that commutes with a group operation on the search space. This paper shows that structural crossover and mutation project naturally onto competi...

Evolutionary and genetic algorithms (EAs and GAs) are quite successful randomized function optimizers. This success is mainly based on the interaction of different operators like selection, mutation, and crossover. Since this interaction is still not well understood, one is interested in the analysis of the single operators. Jansen and Wegener (2001a) have described so-called real royal road fu...