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Publications

Publications by Rui Lourenço Lopes

2019

Guest Editorial: Special Issue on Data Mining for Geosciences

Authors
Jorge, A; Lopes, RL; Larrazabal, G; Nikhalat Jahromi, H;

Publication
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract

2020

Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming

Authors
Lopes, RL; Figueira, G; Amorim, P; Almada Lobo, B;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than , while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.

2011

Using Feedback in a Regulatory Network Computational Device Generative and Developmental Systems

Authors
Lopes, RL; Costa, E;

Publication
GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

Abstract
The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence problems, we show experimentally the effectiveness of the proposal.

2011

MODELING SETTLEMENT PATTERNS IN REAL TERRITORIES

Authors
Carvalho, J; Lopes, RL; Tojo, J;

Publication
ADVANCES IN COMPLEX SYSTEMS

Abstract
This paper, describes an agent based model of the spreading of a population over a territory. The models aims at reproducing a distribution of settlements with statistical and spatial characteristics similar to a historically produced pattern. The model operates on a representation of a real territory, taking into account hydrography and relief. The two main goals are to obtain a rank size distribution of the size of settlements which corresponds to a power law (also known as the Zipf Law of settlements) and to place the settlements in the territory in patterns that are close to the real ones, in zones where settlements were the result of a long historical process. The goal of the project was to demonstrate that a set of relatively simple rules could produce a complex pattern, similar to the result of a long and complex historical process. Therefore, it is an assumed reductionist approach. Our conclusions show that a simple territorial logic, taking into account the quality of land, accessibility, population growth and migration preferences could reproduce Zipf distributions and interesting patterns of agent flow among the settlements created. However, achieving spatial patterns closer to the historical record needs an extra dimension involving field of sight. The best results were achieved by creating an artifical population which chooses to create settlements in places where a wide field of view exists of quality territory.

2010

The office-space-allocation problem in strongly hierarchized organizations

Authors
Lopes, R; Girimonte, D;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The office-space-allocation (OSA) problem will be introduced for strongly hierarchized organizations. In several organizations the relation between the entities is strongly hierarchized, affecting the design and handling of constraints and algorithms used to solve the problem. Moreover there is also an increase in the constraint number when compared to the common test instances. Several well known meta-heuristics were used, new constraints developed, and some variations to the local search algorithms were studied. This article describes the work done and its application to a particular case study, the European Space Research and Technology Center (ESTEC). © 2010 Springer-Verlag Berlin Heidelberg.

2011

ReNCoDe: A Regulatory Network Computational Device

Authors
Lopes, RL; Costa, E;

Publication
GENETIC PROGRAMMING

Abstract
In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.

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