The coreworld: emergence and evolution of cooperative structures in a computational chemistry
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Adaptive individuals in evolving populations
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Model of Evolutionary Emergence of Purposeful Adaptive Behavior. The Role of Motivation
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
A Market Protocol for Decentralized Task Allocation
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Embodied evolution and learning: the neglected timing of maturation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
An investigation of fitness sharing with semantic and syntactic distance metrics
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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Evolution can be employed for two goals. Firstly, to provide a force for adaptation to the environment as it does in nature and in many artificial life implementations - this allows the evolving population to survive. Secondly, evolution can provide a force for optimisation as is mostly seen in evolutionary robotics research - this causes the robots to do something useful. We propose the MONEE algorithmic framework as an approach to combine these two facets of evolution: to combine environment-driven and task-driven evolution. To achieve this, MONEE employs environment-driven and task-based parent selection schemes in parallel. We test this approach in a simulated experimental setting where the robots are tasked to collect two different kinds of puck. MONEE allows the robots to adapt their behaviour to successfully tackle these tasks while ensuring an equitable task distribution at no cost in task performance through a market-based mechanism. In environments that discourage robots performing multiple tasks and in environments where one task is easier than the other, MONEE's market mechanism prevents the population completely focussing on one task.