Specific topics addressed in the book include:
- the hurdles faced in solving large-scale, cutting edge applications
- promising techniques, including fitness and age layered populations, code reuse through caching, archives and run transferable libraries, Pareto optimization, and pre- and post-processing
- the use of information theoretic measures and ensemble techniques
- approaches to help GP create trustable solutions
- the use of expert knowledge to guide GP
- ways to make GP tools more accessible to the non-GP-expert
- practical methods for understanding and choosing between the recent proliferation of techniques for improving GP performance
- the potential for GP to undergo radical changes to accommodate the expanded understanding of biological genetics and evolution
The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
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