Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities

Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

Eiben, A.E. [Promotor

Imperfect Evolutionary Systems

In this paper, we propose a change from a perfect paradigm to an imperfect paradigm in evolving intelligent systems. An imperfect evolutionary system (IES) is introduced as a new approach in an attempt to solve the problem of an intelligent system adapting to new challenges from its imperfect environment, with an emphasis on the incompleteness and continuity of intelligence. We define an IES as a system where intelligent individuals optimize their own utility, with the available resources, while adapting themselves to the new challenges from an evolving and imperfect environment. An individual and social learning paradigm (ISP) is presented as a general framework for developing IESs. A practical implementation of the ISP framework, an imperfect evolutionary market, is described. Through experimentation, we demonstrate the absorption of new information from an imperfect environment by artificial stock traders and the dissemination of new knowledge within an imperfect evolutionary market. Parameter sensitivity of the ISP framework is also studied by employing different levels of individual and social learning

Imperfect Evolutionary Systems

In this paper, we propose a change from a perfect paradigm to an imperfect paradigm in evolving intelligent systems. An imperfect evolutionary system (IES) is introduced as a new approach in an attempt to solve the problem of an intelligent system adapting to new challenges from its imperfect environment, with an emphasis on the incompleteness and continuity of intelligence. We define an IES as a system where intelligent individuals optimize their own utility, with the available resources, while adapting themselves to the new challenges from an evolving and imperfect environment. An individual and social learning paradigm (ISP) is presented as a general framework for developing IESs. A practical implementation of the ISP framework, an imperfect evolutionary market, is described. Through experimentation, we demonstrate the absorption of new information from an imperfect environment by artificial stock traders and the dissemination of new knowledge within an imperfect evolutionary market. Parameter sensitivity of the ISP framework is also studied by employing different levels of individual and social learning

Imperfect Evolutionary Systems

Abstract—In this paper, we propose a change from a perfect paradigm to an imperfect paradigm in evolving intelligent systems. An imperfect evolutionary system (IES) is introduced as a new approach in an attempt to solve the problem of an intelligent system adapting to new challenges from its imperfect environment, with an emphasis on the incompleteness and continuity of intelligence. We define an IES as a system where intelligent individuals optimize their own utility, with the available resources, while adapting themselves to the new challenges from an evolving and imperfect environment. An individual and social learning paradigm (ISP) is presented as a general framework for developing IESs. A practical implementation of the ISP framework, an imperfect evolutionary market, is described. Through experimentation, we demonstrate the absorption of new information from an imperfect environment by artificial stock traders and the dissemination of new knowledge within an imperfect evolutionary market. Parameter sensitivity of the ISP framework is also studied by employing different levels of individual and social learning. Index Terms—Artificial intelligence, environmental variables, evolutionary computation (EC), imperfect evolutionary systems (IESs), individual learning, social learning. I

An investigation of imperfect evolutionary systems

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