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Axelrod’s originally experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that while PBIL performs well, GA in general does slightly better although more experiments should be(More)
Bargaining is a fundamental activity in social life. Game-theoretic methodology has provided perfect solutions for certain abstract models. Even for a simple model, this method demands substantial human intelligent effort in order to solve game-theoretic equilibriums. The analytic complexity increases rapidly when more elements are included in the models.(More)
  • Nanlin Jin
  • Congress on Evolutionary Computation
  • 2005
The main purpose of this work is to measure the effect of bargaining players’ information completeness on agreements in evolutionary environments. We apply Co-evolutionary Algorithms to solve four incomplete information bargaining problems. Empirical analyses indicate that without complete information of player(s)’s preferences, co-evolving populations are(More)
In this paper, we apply an Evolutionary Algorithm (EA) to solve the Rubinstein’s Basic AlternatingOffer Bargaining Problem, and compare our experimental results with its analytic game-theoretic solution. The application of EA employs an alternative set of assumptions on the players’ behaviors. Experimental outcomes suggest that the applied co-evolutionary(More)
Axelrod’s original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in(More)
This paper proposes and compares feature construction and calibration methods for clustering daily electricity load curves. Such load curves describe electricity demand over a period of time. A rich body of the literature has studied clustering of load curves, usually using temporal features. This limits the potential to discover new knowledge, which may(More)
This paper shows how, with the aid of computer models developed in close collaboration with decision makers and other stakeholders, it is possible to quantify and map how policy decisions are likely to affect multiple ecosystem services in future. In this way, potential trade-offs and complementarities between different ecosystem services can be identified,(More)
This paper presents an evolutionary algorithms based constrain-guidedmethod (CGM) that is capable of handling both hard and soft constraints in optimization problems. While searching for constraintsatisfied solutions, the method differentiates candidate solutions by assigning them with different fitness values, enabling favorite solutions to be(More)
This paper introduces Incentive Method to handle both hard and soft constraints in an evolutionary algorithm for solving some multiconstraint optimization problems. The Incentive Method uses hard and soft constraints to help allocating heuristic search effort more effectively. The main idea is to modify the objective fitness function by awarding(More)