Explorations in evolutionary robotics
Adaptive Behavior
Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
An Behavior-based Robotics
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An architecture to coordinate fuzzy behaviors to control an autonomous robot
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Evolution of fuzzy behaviors for multi-robotic system
Robotics and Autonomous Systems
Evolutionary multi-objective optimization in robot soccer system for education
IEEE Computational Intelligence Magazine
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Multiobjective Evolution of Neural Controllers and Task Complexity
IEEE Transactions on Robotics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Incremental Evolutionary Design of TSK Fuzzy Controllers
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
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Evolutionary computation has been often used for the design of mobile robot controllers thanks to its flexibility and global search ability. A lot of studies have been done based on single-objective functions including weighted-sum scalarizing objective functions. For an example of mobile robot navigation, at least the minimization of the arrival time to the target and the minimization of dangerous situations should be considered. In this case, a weighted-sum of two objectives is always minimized. It is, however, difficult to specify an appropriate weight vector beforehand. This paper demonstrates the application of evolutionary multiobjective optimization to mobile robot navigation in order to optimize the conflicting objective simultaneously. We analyze the obtained non-dominated controllers through simulation experiments in multiagent environments. We also show the utilization of the obtained non-dominated controllers for situation change.