Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
ALIFE Proceedings of the sixth international conference on Artificial life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
U-Mart Project: Learning Economic Principles from the Bottom by Both Human and Software Agents
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
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To construct agents that have trading strategies with adequate rationality and variety is an intrinsic requirement for artificial market study. Difference of preference to return and risk among agents will be one candidate reason of variety of the trading strategies. It can be treated as a multi-objective optimization problem taking both criteria as objective functions. This paper proposes a multi-objective genetic algorithm (MOGA) approach to construction of trading agents for an artificial market. The U-Mart system, an artificial market simulator, is used for a test bed. Agents are evaluated in the U-Mart with other agents having simple strategies, and evolved with the MOGA. Computer simulation shows that various agents having non-dominated trading strategies can be obtained with this approach.