Dispatch scheduling policies offer a computationally inexpensive approach to many scheduling problems as an alternative to more processor intensive search algorithms. They are especially useful in dynamic situations where problem solving may be temporally constrained. However, dispatch heuristics have also been effectively used for guidance within more intensive search algorithms, expanding the...

MOTIVATION Physical mapping of chromosomes using the maximum likelihood (ML) model is a problem of high computational complexity entailing both discrete optimization to recover the optimal probe order as well as continuous optimization to recover the optimal inter-probe spacings. In this paper, two versions of the genetic algorithm (GA) are proposed, one with heuristic crossover and determinist...

This paper presents an improved multi-objective evolutionary algorithm (IMOEA) for the design of planar steel frames. By considering constraints as a new objective function, single objective optimization problems turned to multi objective optimization problems. To increase efficiency of IMOEA different Crossover and Mutation are employed. Also to avoid local optima dynamic interference of mutat...

The genetic algorithm is a powerful heuristic for the solution of hard combinatorial problems and has been investigated by numer ous authors. Many problems, arising for example in communi. cation networks, possess stro ng two-dimensional characteristics. We describe a genetic algorit hm with a new crossover operator called block-uniform crossover, which exploits the two-dimensional character of...

In this paper, we consider each neural network as a point in a multi-dimensional problem space and suggest a crossover that locates the central point of a number of neural networks. By this, genetic algorithms can spend more time around attractive areas. We also apply representational normalization to neural networks to maintain genotype consistency in crossover. For the normalization, we utili...

Genetic Algorithms (GAs) are a commonly used stochastic search heuristic which have been applied to a plethora of problem domains. GAs work on a population of chromosomes (an encoding of a solution to the problem at hand) and breed solutions from fit parents to hopefully produce fitter children through a process of crossover and mutation. This work discusses two novel crossover approaches for G...

The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value calculat...

being one of the major research fields in the robotics discipline, the robot motion planning problem deals with finding an obstacle-free start-to-goal path for a robot navigating among workspace obstacles. such a problem is also encountered in path planning of intelligent vehicles and automatic guided vehicles (agvs). traditional (exact) algorithms have failed to solve the problem effectively s...

Similarities are more important than differences. The importance of these common components is set forth by the commonality hypothesis: schemata common to aboveaverage solutions are above average. This hypothesis is corroborated by the isolation of commonality-based selection. It follows that uncommon components should be below average (relative to their parents). In genetic algorithms, the tra...

According to the characteristics of flexible job shop scheduling (FJPS), a mathematical model was established minimize maximum completion time, and an improved genetic algorithm proposed solve problem. A variety heuristic methods are used improve quality initial solution. The parallel double-chain encoding is designed optimal insertion method Two crossover methods, namely IPOX multi-point cross...