Alexandre César Muniz de Oliveira

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This paper describes an application of a Constructive Genetic Algorithm (CGA) to the Gate Matrix Layout Problem (GMLP). The GMLP happens in very large scale integration (VLSI) design, and can be described as a problem of assigning a set of circuit nodes (gates) in an optimal sequence, such that the layout area is minimized, as a consequence of optimizing(More)
This work describes a new way of employing problem-specific heuristics to improve evolutionary algorithms: the Population Training Heuristic (PTH). The PTH employs heuristics in fitness definition, guiding the population to settle down in search areas where the individuals can not be improved by such heuristics. Some new theoretical improvements not present(More)
This paper describes an application of a Constructive Genetic Algorithm (CGA) to the Minimization Open Stack Problem (MOSP). The MOSP happens in a production system scenario, and consists of determining a sequence of cut patterns that minimizes the maximum number of opened stacks during the cutting process. The CGA has a number of new features compared to a(More)
Genetic algorithms, inspired by the theory of evolution of species, are intended to be unfair. Individuals compete against each other and the best-adapted ones prevail. Unfairness is due to big differences of skills, generally evaluated by a fitness measure, in a population of individuals competing for survival. However, population diversity is important to(More)
Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Moran's index and Geary's coefficient measures in breast tissues extracted from mammogram images. These measures(More)
A challenge in hybrid evolutionary algorithms is to define efficient strategies to cover all search space, applying local search only in actually promising search areas. This paper proposes a way of detecting promising search areas based on clustering. In this approach, an iterative clustering works simultaneously to an evolutionary algorithm accounting the(More)
A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent and efficient computational models. In this chapter, the Clustering Search (*CS) is proposed as a generic way of(More)
Overview The Berth Allocation Problem (BAP) consists on programming and allocating ships to berthing areas along a quay. The BAP is modeled as a vehicle routing problem and a recently proposed evolutionary hybrid method denominated PTA/LP is used to solve it. The PTA/LP combines the Population Training Algorithm with Linear Programming to generate improving(More)
This paper describes an application of two evolutionary algorithms to unconstrained numerical optimization. The first is a steady-state genetic algorithm that combines some well-succeeded features of other genetic algorithms. The other is a heuristic training evolutionary approach that evolves a dynamic population to local optimal points. The algorithms are(More)