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A model for the emergence of adaptive subsystems
H Dopazo1, M B Gordon, R Perazzo
1Departamento de Biología, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón 2, Ciudad Universitaria, 1428 Buenos Aires, Argentina. hdopazo@dna.uba.ar
Bulletin of Mathematical Biology
|February 25, 2003
Summary
Learning and evolution interact in changing environments. A population may either lose learning ability or develop a flexible system to adapt, depending on environmental change costs. This reveals how adaptive subsystems emerge.
Area of Science:
- Evolutionary biology
- Computational neuroscience
- Artificial intelligence
Background:
- Learning capability can be an emergent adaptive system.
- Natural selection acts on genetic variants in a changing environment.
- Genotypes possess fixed and flexible alleles influencing synaptic connections.
Purpose of the Study:
- Investigate the interplay between learning and evolution in dynamic environments.
- Model the emergence of adaptive learning capabilities through natural selection.
- Understand the evolutionary pathways of learning in response to environmental shifts.
Main Methods:
- Utilized genetic algorithms to model evolution in an asexual population.
- Represented environmental change using alternating optimal synaptic patterns.
- Extended the Hinton and Nowlan model with a focus on Hamming distance and environmental change rates.
Main Results:
- Identified two distinct evolutionary pathways based on environmental change difficulty.
- Observed populations either losing learning ability or developing flexible adaptation.
- Demonstrated that fixed synapses become optimal in one environmental state.
Conclusions:
- Adaptive subsystems can emerge from the trade-off between exploiting innate structures and exploring learning capabilities.
- Environmental change dynamics dictate whether learning ability is retained or lost.
- The model provides insights into the evolution of adaptive systems in fluctuating conditions.