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Natural Computation: computational models inspired by nature Dr. Daniel Tauritz Department of Computer Science University of Missouri-Rolla CS347 Lecture May 2003 What is Natural Computation? “Characteristic for man-designed computing inspired by nature is the metaphorical use of concepts, principles and mechanisms underlying natural systems” Quoted from the Leiden Center for Natural Computing The Bioinformatics link • Both straddle the fields of Computer Science & Biology • Bioinformatics’ computational demands are often ill-suited for conventional computational models • Natural Computation offers solutions capable of dealing with extremely large data sets, high dimensionality, complex pattern recognition, and sophisticated classification The Artificial Intelligence Link • Classic (symbolic) AI: game play, diagnostic expert systems, etc. • “True” intelligence eludes classic AI • Nature has produced “true” intelligence • Hopefully nature inspired computational models can achieve “true intelligence” too! Nature’s way of computing • Slow symbolic steps, but extremely parallel (resulting in weak numeric performance, but strong pattern recognition and classification capabilities) • High computational error rates, but very fault-tolerant (good at fuzzy logic) • Imperfect memory, but strong ability to learn/adapt (on the individual and/or population level) Nature inspired models • • • • Quantum Computing DNA/Molecular Computing Artificial Life Swarm Intelligence - Ant Colony Optimization - Particle Swarm Optimization • Artificial Immune Systems (Computational Immunology) • Artificial Neural Networks (Connectionism) • Evolutionary Computation • Quantum Computing based on quantum physics, exploits quantum parallelism; aims at non-traditional hardware that would allow quantum effects to take place • DNA/Molecular Computing based on paradigms from molecular biology; aims at alternatives for silicon hardware by implementing algorithms in biological hardware (bioware), e.g., using DNA molecules and enzymes • Artificial Life - attempts to model living biological systems through complex algorithms (examples: stem cell simulation, computational epidemics, gene regulatory system simulation, stock market simulation, predatorprey studies, etc.) Swarm Intelligence • Ant Colony Optimization – population based optimization technique inspired by the behavior of ant colonies • Particle Swarm Optimization – population based optimization technique inspired by social behavior of bird flocking or fish schooling • Artificial Immune Systems (Computational Immunology) • Artificial Neural Networks (Connectionism) Evolution • • • • • • • Individuals Population Environment Fitness Selection - selective pressure Reproduction Competition – survival of the fittest Heredity • Asexual versus sexual reproduction • Genes • Loci • Alleles • Genotype versus phenotype • Genetic operators: replication, recombination, mutation Evolutionary Computation • Solving “difficult” problems • Search spaces: representation & size • Evaluation of trial solutions: fitness function • Exploration versus exploitation • Selective pressure rate • Premature convergence Environment Problem (search space) Fitness Fitness function Population Set Individual Datastructure Genes Elements Alleles Datatype Evolutionary cycle evaluation selection initialization competition reproduction mutation Pros • General purpose: minimal knowledge required • Ability to solve “difficult” problems • Solution availability • Robustness Cons • Fitness function and genetic operators often not obvious • Premature convergence • Computationally intensive • Difficult parameter optimization Evolving versus learning • In EC learning occurs at the population level instead of at the individual level • In nature evolution and learning are combined • Darwinian evolution evolves the blue print of a learning system • Baldwin effect: phenotypic plasticity (e.g., learning [local search]) • Lamarckian evolution involves direct inheritance of characteristics acquired by individuals during their lifetime Natural Computation Courses Fall 2003 • CS378/Eng.Mg.378/El.Eng.368 Introduction to Neural Networks & Applications Dr. Dagli – Eng.Mg. • CS401 Introduction to Evolutionary Computation Dr. Tauritz - CS