Colorado State University, USA
Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, displaying nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Understanding how genotype-phenotype maps partition the space of sequences into complex genotype networks is essential to update evolutionary theory. Such studies, however, have been mainly computational and, as such, are and will always be limited by the unfathomable size of sequence spaces. Theoretical advances are contributing to grasp the quantitative nature of adaptive molecular processes, whose dynamics is deeply dependent on the intrinsic network-of-networks multilayered structure of sequence spaces that we begin to unveil.
Susanna Manrubia studied physics at the Universitat de Barcelona, and received her doctoral degree from UPC in 1996. She was a Humboldt fellow of the Max Planck Society at the Fritz-Haber-Institut in Berlin and a postdoctoral researcher at the MPI of Colloids and Interfaces in Golm. After several years at the Center for Astrobiology in Madrid, she is since 2014 associate professor of Molecular Biology and Biotechnology at the National Centre for Biotechnology (CSIC, Madrid). She focuses on developing theoretical and computational descriptions of biological phenomena, from the genome to large-scale evolution, and maintains close collaborations with experimentalists. Her interests include as well the emergence of cultural patterns and collective social behaviour. She has published over 130 peer reviewed articles and three books, was Section Editor for BMC Evolutionary Biology and is current member of the Editorial Board of Virus Evolution.
Every EC researcher should look in the mirror and ask: What does Evolutionary Computation have to offer to the larger field of inexact methods for combinatorial optimization? This talk will take a new look at recombination operators, and in particular how they can be used to complement Iterated Local Search. In some cases, we can design powerful deterministic recombination operators that can be used with small dynamically emergent populations. In other cases, we need to use recombination in new, more intensive ways. This talk will also look at how search methods with different bias patterns can be exploited as parallel ensembles of solvers on multicore machines.
Darrell Whitley is a Professor of Computer Science at Colorado State University. He served as the Chair of the Governing Board of the International Society of Genetic Algorithms (ISGA) from 1993 to 1997, as the Editor-in-Chief of the journal Evolutionary Computation from 1997 to 2003 and as Co-Editor-in-Chief of the ACM Transactions on Evolutionary Learning and Optimization, 2019-2021. He was Chair of the Executive Board of ACM SIGEVO from 2007 to 2011. He was named an ACM Fellow in 2019 for his contributions to the field of genetic and evolutionary computation.