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The widespread adoption of 'Design Patterns' heralded a revolution in the software industry, offering a catalog of designs that cut across lower-level concerns such as choice of programming language, platform etc.

In a similar manner, we anticipate that ...

Welcome to the Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS 2014)!

Evolutionary computation (EC) and multi-agent systems and simulation (MASS) both involve populations of agents. EC is a learning ...

A framework is introduced for applying GP to streaming data classification tasks under label budgets. This is a fundamental requirement if GP is going to adapt to the challenge of streaming data environments. The framework proposes three elements: a ...

This paper expands on work previously conducted on the XCS system using code fragments, which are GP-like trees that encapsulate building blocks of knowledge. The usage of code fragments in the XCS system enabled the solution of previously intractable, ...

Interrelationships between rules can be used to develop network models that can usefully represent the dynamics of Learning Classifier Systems. We examine two different kinds of rule networks and study their significance by testing them on the 20-mux ...

In this paper, the ε constrained method and Adaptive operator selection (AOS) are used in Multiobjective evolutionary algorithm based on decomposition (MOEA/D). The ε constrained method is an algorithm transformation method, which can convert algorithms ...

In this paper, we propose a multiobjective probabilistic Pareto local search to address combinatorial optimization problems (COPs). The probability is determined by the success counts of local search offspring entering an external domination archive and ...

There have been several papers published relating to the practice of benchmarking in machine learning and Genetic Programming (GP) in particular. In addition, GP has been accused of targeting over-simplified 'toy' problems that do not reflect the ...

Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolutionary search methods and ...

Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to ...

A novel approach is proposed for generating equations from measured data of dynamic processes. A composition of unary (alpha) and binary (beta) functions is represented by a real vector and adapted by an evolutionary algorithm to build mathematical ...

Improvement of processes in metallurgical industry is a constant of competitive enterprises, however, changes made in a process are risky and involves high cost and time, considering this, a model can be made even using inputs usually not presented in ...

In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalize well on previously unseen data. GP with data migration uses ...

Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, a lot of research has been made to improve the ...

Image segmentation is a common image processing step to many computer vision applications with the purpose to segment pixels into different classes. As improved variants of particle swarm optimization (PSO) algorithms, the fractional-order Darwinian ...

Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible ...

In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In ...