Conference Archives - VINÍCIUS VELOSO DE MELO

A CMA-ES-based 2-stage memetic framework for solving constrained optimization problems

Posted on 28 de January de 2015 by admin Posted in CMAES, Conference, Evolutionary algorithm, Local-search, Memetic, Metaheuristic

Constraint optimization problems play a crucial role in many application domains, ranging from engineering design to finance and logistics. Specific techniques are therefore needed to handle complex fitness landscapes characterized by multiple constraints. In the last decades, a number of novel meta-heuristics have been applied to constraint optimization. Among these, the Covariance Matrix Adaptation Evolution

Kaizen Programming

Posted on 23 de July de 2014 by admin Posted in Conference, Evolutionary algorithm, Genetic programming, Machine learning, Regression

This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus

Automatic generation of evolutionary operators: a study with mutation strategies for the differential evolution

Posted on 14 de January de 2013 by admin Posted in Artifical neural networks, Classification, Conference, Differential evolution, Genetic programming, Global Continuous Unconstrained Optimization, Machine learning, Metaheuristic, Regression, Self-adaptive

Benchmarking the multi-view differential evolution on the noiseless BBOB-2012 function testbed

Posted on 14 de January de 2013 by admin Posted in Conference, Differential evolution, Global Continuous Unconstrained Optimization, Metaheuristic, Multi-view, Self-adaptive

We propose a multi-view DE in which several mutation strategies are applied to the same current population to generate different views for the current iteration. The views are then merged using tournament-selection to generate the next single population. This Multi-View DE is benchmarked on the BBOB-2012 noiseless function testbed. Keywords: procés de test.

Efficiency Enhancement of ECGA Through Population Size Management

Posted on 14 de January de 2013 by admin Posted in Conference, Efficiency enhancement, Evolutionary algorithm, Global Continuous Unconstrained Optimization, Metaheuristic

This paper describes and analyzes population size management, which can be used to enhance the efficiency of the extended compact genetic algorithm (ECGA). The ECGA is a selectorecombinative algorithm that requires an adequate sampling to generate a high-quality model of the problem. Population size management decreases the overall running time of the optimization process by

VINÍCIUS VELOSO DE MELO

Category Archives: Conference

A CMA-ES-based 2-stage memetic framework for solving constrained optimization problems

Kaizen Programming

Automatic generation of evolutionary operators: a study with mutation strategies for the differential evolution

Benchmarking the multi-view differential evolution on the noiseless BBOB-2012 function testbed

Efficiency Enhancement of ECGA Through Population Size Management

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