Evolution in Cognition 2016 Chairs' Welcome

Evolution by natural selection has shaped life over billions of years leading to the emergence of complex organisms capable of exceptional cognitive abilities. These natural evolutionary processes have inspired the development of Evolutionary Algorithms (EAs), which are optimization algorithms widely popular due to their efficiency and robustness. Beyond their ability to optimize, EAs have also proven to be creative and efficient at generating innovative solutions to novel problems. The combination of these two abilities makes EAs a tool of choice for the resolution of complex problems. Even though there is evidence that the principle of selection on variation is at play in the human brain, as proposed in Changeux's and Edelman's models of Neural Darwinism [1, 8], and more recently expanded in the theory of Darwinian Neurodynamics by Szathmáry, Fernando and others [9], not much attention has been paid to the possible interaction between evolutionary processes and cognition over physiological time scales. Since the development of human cognition requires years of maturation, it can be expected that artificial cognitive agents will also require months if not years of learning and adaptation. It is in this context that the optimizing and creative abilities of EAs could become an ideal framework that complement, aid in understanding, and facilitate the implementation of cognitive processes. Additionally, a better understanding of how evolution can be implemented as part of an artificial cognitive architecture can lead to new insights into cognition in humans and other higher organisms. The goals of the workshop are to depict the current state of the art of evolution in cognition and to sketch the main challenges and future directions. In particular, we aim at bringing together different theoretical and empirical approaches that can potentially contribute to the understanding of how evolution and cognition can act together in an algorithmic way in order to solve complex problems. In this workshop we welcome approaches that contribute to an improved understanding of evolution in cognition using robotic agents, in silico computation, as well as mathematical models.