International Journal of Computational Intelligence and Applications

This paper presents a method on how to estimate main effects of gene representation. This estimate can be used not only to understand the domination of genes in the representation but also to design the mutation rate in genetic algorithms (GAs). A new approach of dynamic mutation rate is proposed by integrating the information of the main effects into the genes. By introducing the proposed method in GAs, both solution quality and solution stability can be improved in solving a set of parametrical test functions. The algorithm was applied to two illustrative applications to evaluate the performance of the proposed method, where the first application is on solving uncapacitated facility location problems and the next is on optimal power flow problems, which are employed. Results indicate that the proposed method yields significantly better results than the existing methods.

Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.

Problems that are not aligned with the coordinate system can present difficulties to many optimization algorithms, including evolutionary algorithms, by trapping the search on a ridge. The ridge problem in single-objective optimization is understood, but until now little work has been done on understanding this issue in the multi-objective domain. Multi-objective problems with parameter interactions present difficulties to an optimization algorithm, which are not present in the single-objective domain. In this work, we have explained the nature of these difficulties, and investigated the behavior of the NSGA-II, which has difficulties with problems not aligned with the principle coordinate system. This study has investigated Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-Centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II, on problems exhibiting parameter interactions through a rotation of the coordinate system. An analysis of these operators on three rotated bi-objective test problems, and a four-and eight-objective problem is provided. New observations on the behavior of rotationally invariant crossover operators in the multi-objective problem domain have been reported.

This paper proposes a hybrid estimation of distribution algorithm (HyEDA) to address the design problem of code division multiple access cellular system configuration. Given a service area, the problem is to find a set of optimal locations of base stations, associated with their corresponding powers and antenna heights in the area, in order to maximize call quality and service coverage, at the same time, to minimize the total cost of the system configuration. HyEDA is a two-stage hybrid approach which integrates an estimation of distribution algorithm, a K-means clustering method, and a simple local search algorithm. We have compared HyEDA with a simulated annealing method on a number of instances. Our simulation results have demonstrated that HyEDA outperforms the simulated annealing method in terms of the solution quality and computational cost.

The genetic programming (GP) evolutionary process typically introduces a large amount of redundancy and unnecessary complexity into evolved programs. Quick growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Rather than implicitly controlling the redundancy and code growth/bloat as in most of the existing approaches, this paper investigates an algebraic simplification algorithm for explicitly removing the redundancy from the genetic programs and simplifying these programs online during the evolutionary process. The new GP system with the simplification is examined and compared with a standard GP system on two regression and three classification problems of varying difficulties. The results show that the GP system employing a simplification component can achieve superior efficiency with comparable or slightly superior effectiveness to the standard GP system on these problems. The programs evolved by the new GP approach with the explicit simplification contain hidden patterns'' for a particular problem and are relatively simple and easy to interpret.