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Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2)...
Multipopulation methods are effective in solving dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this paper, an adaptive multipopulation framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number...
Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in...
In this paper we propose a novel evolutionary algorithm that is able to adaptively separate the explored and unexplored areas to facilitate detecting changes and tracking the moving optima. The algorithm divides the search space into multiple regions, each covers one basin of attraction in the search space and tracks the corresponding moving optimum. A simple mechanism was used to estimate the basin...
We study decentralized markets which include middlemen, producers and consumers connected via a trading network. We develop a model for trade in such settings based on non-cooperative bargaining with search frictions. Our goal is to investigate how the structure of the trading network and the role of middlemen influence the market's efficiency and fairness. To this end, we introduce the concept of...
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