Recombination Hotspots Promote the Evolvability of Modular Systems
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, , 115-116, 2016
Abstract: Random recombination in evolutionary algorithms can be counterproductive in systems that evolve increasing modularity, because such operators do not preserve community structures during their development. Partly because of this, methods have been proposed that derandomize recombination by placing potential crossover locations under evolutionary control. Since crossover is likely to be particularly useful when genetic material that generates incipient phenotype modules is recombined, there may be an advantage to seeking such modularity directly in the phenotype and probabilistically focusing recombination at such "hotspot" locations. Here we show that such phenotypically-aware crossover operators can outcompete random or evolved crossover points as the size of the system being evolved grows. As this crossover operator can be viewed as epigenetic, and as epigenetic processes seem to be common in biological systems, other such epigenetic mechanisms may further improve future evolutionary algorithms.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).