Examining the Impact of Positive and Negative Constant Learning on the Evolution Rate

• Autorzy: Gajer M.
• Języki publikacji: EN

Abstrakty

EN

The paper discusses the influence of learning on evolutionary processes. In biological sciences it is a well-known fact that the rate of evolution can be effected by learning and the same phenomena can also be observed in artificial evolutionary systems, however, their nature is still not sufficiently well understood. In the paper the influence of constant learning on the rate of evolution is examined. The constant learning is a kind of learning during which the genotype of the individual being taught is moved toward the global optimum over a constant value. If the fitness function is monotonic, it can be concluded from the mathematical theory that such kind of learning should decelerate evolution. However, this fact is highly counterintuitive and for this reason it should be proved by numerical experiments. In the article the results of numerical simulations are presented. They prove that evolution is indeed decelerated by learning in case of the sigmoid fitness function. Moreover, two cases of constant learning were examined in the paper. These are the positive and negative constant learning. It was demonstrated that in the case of the negative constant learning the evolution was decelerated to a larger extent than in the case of the positive constant learning. The obtained results can help explain certain phenomena concerning the impact of learning on the evolution both in natural and artificial evolutionary systems.

Słowa kluczowe

EN

evolutionary systems; learning process; constant learning

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2009
• Tom: Vol. 13, No 4
• Strony: 355--362
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Gajer M.

• AGH University of Science and Technology, Department of Control Science, Al. Mickiewicza 30, 30059 Cracow, Poland,

Bibliografia

• [1] Stanley K O, D’Ambrosio D B and Gauci J 2009 Artificial Life 15 185
• [2] Paenke I, Kawecki T J, and Sendhoff B 2009 Artificial Life 15 227
• [3] Hinton G and Nowlan S 1987 Complex Systems 1 495
• [4] Papaj D 2000 Optimizing Learning and its Effect on Evolutionary Change in Behavior,Behavioral Mechanisms in Evolutionary Ecology, University of Chicago Press, pp. 133–154
• [5] Mery F and Kawecki T 2004 Evolution 58 757
• [6] Winkler D A, Burden F R and Halley J D 2009 Artificial Life 15 411
• [7] Ampatzis C, Tuci E, Trianni V, Christensen A L and Doringo M 2009 Artificial Life 15 465
• [8] Bullinaria J A 2009 Artificial Life 15 389