Planning and control
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Information warfare and security
Information warfare and security
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Learning Algorithms: Theory and Applications in Signal Processing
Learning Algorithms: Theory and Applications in Signal Processing
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Artificial Intelligence
Genetic Search with Approximate Function Evaluation
Proceedings of the 1st International Conference on Genetic Algorithms
Distributed Spatial Control, Global Monitoring and Steering of Mobile Agents
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Effective vaccination policies
Information Sciences: an International Journal
Grammatical inference and games: extended abstract
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
The objective of this project is to develop effective finite-state machine (FSM) strategies for winning against an adversary in a Competition for Resources simulation. To achieve this goal, we evolve these strategies in a simulated environment and compare a variety of evolutionary methods in this context. Key empirical questions are addressed, such as how many FSM states are optimal, how effective is it to use an evolutionary algorithm that adapts the number of states, and how can one reduce the variance in fitness evaluation? Some of our experimental answers to these questions are quite intriguing. This chapter also explores and evaluates novel algorithms for detecting and repairing deleterious cycles in the evolved FSMs.