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The performance of searching agents, or metaheuristics, like evolutionary algorithms (genetics algorithms, genetic programming, etc.) or local search algorithms (simulated annealing, tabu search, etc.) depend on some properties of the search space ...

The performances of evolutionary algorithms (genetics algorithms, genetic programming, etc.) or local search algotihms (Simulated annealing, tabu search, etc.) depends on the properties of seach space structure. One concept to analyse the search space ...

The rapid advances in computing and transmission technologies are giving impetus to the large-scale deployment of interconnected systems for communication and transport of data, voice, video and resources. The global Internet, and cellular, satellite, ...

Cartesian Genetic Programming is a form of genetic programming. It is increasing in popularity. It was developed by Julian Miller with Peter Thomson in 1997. In its classic form it uses a very simple integer based genetic representation of a program in ...

We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-...

Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. ...

This paper presents a scaling technique called Relative Fitness (RF) for roulette-wheel parent selection in genetic algorithm (GA). The RF scaling factor is a function of the fitness value of a chromosome. Without compromising the running time of the ...

The Eltrut problem is an optimization problem concerning the educational robot known as the LOGO turtle. In this study, I describe my formulation of the Eltrut problem and my attempts to solve it using evolutionary computing techniques. Motivation is ...

The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters presents the characteristic of adaptiveness, i.e., the capacity of changing ...

Although Multi-objective Evolutionary Algorithms (MOEAs) have successfully been used in a wide range of real-world problems, recent studies have shown that they have scalability limitations in problems with a large number of objectives. This paper ...

We propose and evaluate the use of a Particle Swarm Optimization/Ant Colony Optimization (PSO/ACO) methodology for classification and rule discovery in the context of medication postmarketing surveillance or pharmacovigilance. Our study considers a ...

Radiologists disagree with each other over the characteristics and features of what constitutes a normal mammogram and the terminology to use in the associated radiology report. Recently, the focus has been on classifying abnormal or suspicious reports, ...

Evolutionary Algorithms are believed to be relatively robust on noisy objective functions, but generally stagnate in the (later) stages of the evolution process when the population has zoomed in on a particular area of the search space when the noise ...

In this paper we look at systems consisting of many autonomous components or agents which have only limited amount of resources (e.g. memory) but are able to communicate with each other. The aim of these systems is to solve classification problems (...

This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed in the ...