Layout problems belong to NP-Complete problems theoretically. They are concerned more and more in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, plant equipments, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are relatively effective to solve these kind of problems. But usua...

Gravitational search algorithm (GSA) has been successfully applied to many scientific and engineering applications in the past few years. In the original GSA and most of its variants, every agent learns from all the agents stored in the same elite group, namely K best . This type of learning strategy is in nature a fully-informed learning strategy, in which every agent has exactly the same glob...

Convergence to equilibrium is an essential requirement for molecular simulation output to be accurate and reproducible. Yet testing for convergence is challenging, especially in instances where kinetic trapping and the consequent plateauing of ensemble quantities can falsely suggest premature equilibrium. Here we introduce a quantitative method for monitoring convergence based on multiple indep...

To improve particle swarm optimization (PSO) computing performance, the centroid of particle swarm is firstly introduced in standard PSO model to enhance interparticle cooperation and information sharing capabilities, then combining randomness and ergodicity of the strong chaotic motion and fast convergence of the simplex method, a novel particle swarm optimization algorithm with adaptive space...

This paper investigates fitness sharing in genetic programming. Implicit fitness sharing is applied to populations of programs. Three treatments are compared: raw fitness, pure fitness sharing, and a gradual change from fitness sharing to raw fitness. The 6and 11-multiplexer problems are compared. Using the same population sizes, fitness sharing shows a large improvement in the error rate for b...

This paper introduces a novel method of visual learning based on Genetic Programming, which evolves a population of individuals (image analysis programs) that process attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognizes the training concept (shape). The approach uses generative evaluation scheme: individuals are r...

This paper proposes a rank-based control method of mutation probability for improving the performances of genetic algorithms (GAs). In order to improve the performances of GAs, GAs should not fall into premature convergence phenomena and should also be able to easily get out of the phenomena when GAs fall into the phenomena without destroying good individuals. For this, it is important to keep ...

s: This paper presents a novel approach to improve the performance of genetic algorithms called genetic algorithms with real-coded binary representation (GARB). The proposed algorithm is capable of maintaining the population diversity during the whole run which protects it from premature convergence. This is achieved by using a special encoding scheme with a high redundancy, which is supported ...

Previous studies have shown that hitchhiking in standard GAs can cause premature convergence. In this study we show that hitchhiking also occurs in adaptive operators, specifically, selective crossover an adaptive recombination operator. We compare selective crossover with uniform crossover (a highly disruptive operator) and show that although selective crossover favours hitchhikers more than u...

Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains t...