Evolutionary computation in zoology and ecology

Area of Science:

• Ecology
• Zoology
• Computer Science
• Evolutionary Computation

Background:

• Evolutionary computation methods mimic natural selection and evolution.
• These methods are increasingly applied in zoology and ecology.
• Merging evolutionary principles with agent-based modeling allows for simulating individual agent competition.

Purpose of the Study:

• To introduce and review evolutionary computation methods.
• To highlight their application in ecological and zoological research.
• To demonstrate their flexibility in addressing complex biological and environmental problems.

Main Methods:

• Four main categories of evolutionary computation are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies.
• Populations are assessed via an objective function, with poor performers removed and survivors reproducing with mutation.
• The cycle repeats until a stopping condition is met, simulating evolutionary processes.

Main Results:

• Case studies demonstrate applications in optimizing ecological parameters like egg shape, mate selection, and species richness.
• Simulations project species range shifts under climate change, incorporating competition and phenotypic plasticity.
• Methods are flexible for studying niche spaces, cladistics, community structure, and invasiveness.

Conclusions:

• Evolutionary computation offers a powerful and flexible framework for ecological and zoological research.
• The methods provide valuable tools for understanding species interactions and responses to environmental changes.
• Further applications span diverse areas from population dynamics to evolutionary biology and conservation.