Abstract: An ongoing discussion in biology concerns whether intrinsic mortality, or senescence, is programmed or not. The death (i.e. removal) of an individual solution is an inherent feature in evolutionary algorithms that can potentially explain how intrinsic mortality can be beneficial in natural systems. This paper investigates the relationship between mutation rate and mortality rate with a steady state genetic algorithm that has a specific intrinsic mortality rate. Experiments were performed on a predefined deceptive fitness landscape, the hierarchical if-and-only-if function (H-IFF). To test whether the relationship between mutation and mortality rate holds for more complex systems, an agent-based spatial grid model based on the H-IFF function was also investigated. This paper shows that there is a direct correlation between the evolvability of a population and an indiscriminate intrinsic mortality rate to mutation rate ratio. Increased intrinsic mortality or increased mutation rate can cause a random drift that can allow a population to find a global optimum. Thus, mortality in evolutionary algorithms does not only explain evolvability, but might also improve existing algorithms for deceptive/rugged landscapes. Since an intrinsic mortality rate increases the evolvability of our spatial model, we bolster the claim that intrinsic mortality can be beneficial for the evolvability of a population.
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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).