Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Economic simulations in Swarm: agent-based modelling and object programming
Economic simulations in Swarm: agent-based modelling and object programming
Agent-Based Computer Simulation of Dichotomous Economic Growth
Agent-Based Computer Simulation of Dichotomous Economic Growth
Artificial Societies: The Computer Simulation of Social Life
Artificial Societies: The Computer Simulation of Social Life
Genetic Algorithms in Noisy Environments
Machine Learning
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
On the robustness of population-based versus point-basedoptimization in the presence of noise
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Guest editorial agent-based modeling of evolutionary economic systems
IEEE Transactions on Evolutionary Computation
Nongovernance rather than governance in a multiagent economicsociety
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Artificial agents have been deployed in simulating social or economic phenomena in order to find optimal policy to govern agents' society. However, with an increase of the complexity of agents' internal behaviors as well as their social interactions, modeling social behaviors and tracking down optimal policies in mathematical form become intractable. In this paper, genetic algorithm is used to find optimal solutions to deter criminals in order to reduce the social cost caused by the crimes in the artificial society. The society is characterized by multiple-equilibria and noisy parameters. Sampling evaluation is used to evaluate every candidate. The results of experiments show that genetic algorithms can quickly find the optimal solutions.