In this paper, vector quantizer optimization is accomplished by a hybrid evolutionary method, which consists of a modified genetic algorithm (GA) with a local optimization module given by an accelerated version of the K-means algorithm. Simulation results regarding image compression based on VQ show that the codebooks optimized by the proposed method lead to reconstructed images with higher pea...

Previous studies of Genetic Algorithm (GA) optimization in nonstationary environments focus on discontinuous, Markovian switching environments. This study introduces the problem of GA optimization in continuous, nonstationary environments where the state of the environment is a function of time. The objective of the GA in such an environment is to select a sequence of values over time that mini...

Genetic Algorithm (GA), which borrows the idea of Darwinian principle of natural selection, is a powerful global search and optimization method. This paper presents a SPMD(Single Program Multiple Data) algorithm which combines GA with local searching algorithm – uphill. The hybrid parallel method not only improves the convergence of GA but also accelerates the convergence speed of GA. Approxima...

This paper studies the nonlinear optimization problems subject to bipolar max-min fuzzy relation equation constraints. The feasible solution set of the problems is non-convex, in a general case. Therefore, conventional nonlinear optimization methods cannot be ideal for resolution of such problems. Hence, a Genetic Algorithm (GA) is proposed to find their optimal solution. This algorithm uses th...

The paper presents a Genetic Algorithm (GA) approach for solving constrained reliability redundancy optimization of general systems. The advanced GA technique uses a dynamic adaptive penalty function to consider the infeasible solutions also and guides the search to optimal or near optimal solution. The penalty technique is applied to keep a certain amount of infeasible solutions in each genera...

Constrained optimization via the Genetic Algorithm (GA) is often a challenging endeavor, as the GA is most directly suited to unconstrained optimization. Traditionally, external penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. This approach requires the somewhat arbitrary selection of penalty draw-down coeffi...

An effective and rigorous approach to determine optimum welding process parameters is implementation of advanced computer aided engineering (CAE) tool that integrates efficient optimization techniques and numerical welding simulation. In this paper, an automated computational methodology to determine optimum arc welding process control parameters is proposed. It is a coupled Genetic Algorithms ...

Abstract—In real world applications, optimization is an inevitable stage in any engineering design. In recent days the optimization theory is also fused into other sciences which require precision in its final result. This topic sounds like a promising domain for research almost in all areas of science and technology. Perhaps several solution methods are proposed for solving problems that requi...

a new structure learning approach for bayesian networks (bns) based on asexual reproduction optimization (aro) is proposed in this letter. aro can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. in aro, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...