Differential Evolution of Constants in Genetic Programming Improves Efficacy and Bloat
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, , 625-626, 2012
Abstract: We employ a variant of Differential Evolution (DE) for co-evolution of real coefficients in Genetic Programming (GP). This GP DE method is applied to 30 randomly generated symbolic regression problems of varying difficulty. Expressions were evolved on sparsely sampled points, but were evaluated for accuracy using densely sampled points over much wider ranges of inputs. The GP DE had successful runs on 25 of 30 problems, ￼whereas GP using Ephemeral Random Constants succeeded on only 6 and the multi-objective GP Eureqa on only 18. Although nesting DE slows down each GP generation significantly, successful GP DE runs required many fewer GP generations than the other methods and, in nearly all cases, the number of nodes in the best evolved trees were smaller in GP DE than with the other ￼GP methods.
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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).