Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential in problems where MOEA/D performs the worst

Earthquake risk induction models with genetic algorithm

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.Este projeto visa desenvolver um modelo de previsão de riscos de terremotos com Algoritmos Geneticos (GA). Modelos de risco de terremotos descrevem o risco de ocorrência de atividades sísmicas em uma determinada área baseado em informações previamente obtidas de terremotos em regiões próximas da área de estudo. GA foi utilizada para aprender um modelo de risco usando informações previamente obtidas como base de treino. Baseado nos resultados obtidos, acreditamos ser possível obter melhores modelos se conhecimentos do domínio da aplicação, como conhecimentos oriundos da literatura ou modelos de distribuição de terremotos, poderem ser incorporados ao processo de aprendizado do Algoritmo Genético. O objetivo principal é definir um método para estimar a probabilidade de ocorrências de terremotos no Japão usando dados históricos de terremotos para um grupo de determinadas regiões geográficas. Este trabalho se baseia no contexto do “Collaboratory for the Study of Earthquake Predictability” (CSEP), que visa padronizar os estudos e testes de modelos de previsão de riscos de terremotos. Durante o desenvolvimento das atividades, passamos por três estágios. (1) Nós propusemos um método baseado em uma aplicação de GA e objetivamos gerar um método estatístico de análise de risco de terremotos. Estes foram analisados por seus valores de log-likelihood, como sugerido pelo Regional Earthquake Likelihood Model (RELM). (2) A seguir, modificiamos a representação do genoma, de uma representação baseada em área para uma representação baseada em ocorrências de terremotos, buscando obter uma convergência mais rápida dos valores de log-likelihood dos candidatos do GA e (3) usamos métodos da sismologia conhecidos para refinar os candidatos gerados pelo GA. Em todas as estapas, os modelos de risco são comparados com dados reais, com modelos gerados pela aplicação do Relative Intensity Algorithm (RI) e com eles próprios. Os dados utilizados foram obtidos pela Japan Metereological Angency (JMA) e são relativos a atividades de terremotos no Japão entre os anos de 2000 e 2013. Nós analisamos as contruibuições de cada modelo proposto usando metodologia descritas pelo CSEP e comparamos os modelos desenvolvidos. Os resultados apontam que modelos com terremotos mais estáveis possuem maiores valores de log-likelihood.This projects aims to develop an earthquake prevision risk model using Genetic Algorithms (GA). Earthquake Risk Models describe the risk of occurrence of seismic events on a given area based on information such as past earthquakes in nearby regions, and the seismic properties of the area under study. We used GA to learn risk models using past earthquake occurrence as training data. Based on the results obtained, we believe that a much better model could be learned if domain knowledge, such as known theories and models on earthquake distribution, were incorporated into the Genetic Algorithm’s training process. The main goal is to define good methods to estimate the probability of earthquake occurrences in Japan using historical data of a group given geographical regions. This work is established in the context of the “Collaboratory for the Study of Earthquake Predictability” (CSEP), which seeks to standardise the studies and tests of earthquake risk prevision models. To achieve the main goal, we passed three stages. (1) We proposed a method based in one application of GA and aims to develop statistical methods of analysis of earthquake risk. The risk models generated by this application were analysed by their log-likelihood values, as suggested by the Regional Earthquake Likelihood Model (RELM). (2) Then, we modify the genome representation from an area-based representation to an earthquake representation aiming to reach a faster convergence of the log-likelihood values of the GA’s candidates and (3) we use known methods from seismology (such as the Omori- Utsu formula) to refine the candidates generated by the GA. In all stages, the risk models are compared with real data, with the models generated by the application of the Relative Intensity Algorithm (RI) and with themselves. The data used was obtained from the Japan Meteorological Agency (JMA) and are related with earthquake activity in Japan between the years of 2000 and 2013. We analyse the contributions from each risk model using the methodologies described in the CSEP and compare their quality. Our results indicate that models with more stable earthquakes obtain higher log-likelihood values

MOEA/D with Random Partial Update Strategy

Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of the algorithm. In this work, we investigate a new, more straightforward partial update strategy, in which a random subset of solutions is selected at every iteration. The performance of the MOEA/D-DE using this new resource allocation approach is compared experimentally against that of the standard MOEA/D-DE and the MOEA/D-DE with relative improvement-based resource allocation. The results indicate that using MOEA/D with this new partial update strategy results in improved HV and IGD values, and a much higher proportion of non-dominated solutions, particularly as the number of updated solutions at every iteration is reduced

Search Trajectories Networks of Multiobjective Evolutionary Algorithms

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis

Decision/objective space trajectory networks for multi-objective combinatorial optimisation

International audienceThis paper adapts a graph-based analysis and visualisation tool, search trajectory networks (STNs) to multi-objective combinatorial optimisation. We formally define multi-objective STNs and apply them to study the dynamics of two state-of-the-art multi-objective evolutionary algorithms: MOEA/D and NSGA2. In terms of benchmark, we consider two- and three-objective ρmnk-landscapes for constructing multi-objective multi-modal landscapes with objective correlation. We find that STN metrics and visualisation offer valuable insights into both problem structure and algorithm performance. Most previous visual tools in multi-objective optimisation consider the objective space only. Instead, our newly proposed tool asses algorithm behaviour in the decision and objective spaces simultaneously