Modelling evolvability in genetic programming

We develop a tree-based genetic programming system, capable of modelling evolvability during evolution through artificial neural networks (ANN) and exploiting those networks to increase the generational fitness of the system. This thesis is empirically focused; we study the effects of evolvability selection under varying conditions to demonstrate the effectiveness of evolvability selection. Evolvability is the capacity of an individual to improve its future fitness. In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability. We construct a system, Sample-Evolvability Genetic Programming (SEGP), that estimates the true evolvability of a program by conducting a limited number of evolvability samples. Evolvability is sampled by conducting a number of genetic operations upon a program and comparing the fitnesses of resulting programs with the original. SEGP is able to achieve an increase in fitness at a cost of increased computational complexity. We then construct a system which improves upon SEGP, Model-Evolvability Genetic Programming (MEGP), that models the true evolvability of a program by training an ANN to predict its evolvability. MEGP reduces the computational cost of sampling evolvability while maintaining the fitness gains. MEGP is empirically shown to improve generational fitness for a streaming domain, in exchange for an upfront increase in computational time

Degeneracy: a link between evolvability, robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology. This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability

The evolutionary origins of hierarchy

Hierarchical organization -- the recursive composition of sub-modules -- is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force--the cost of connections--promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.Comment: 32 page

Evolvability and organismal architecture:The blind watchmaker and the reminiscent architect

Organisms are constantly faced with the challenge of adapting to new circumstances. In this thesis, I argue that the ability to adapt to new circumstances, “evolvability”, is deeply ingrained in the genetic, developmental, morphological, and physiological architecture of organisms. Using a blend of conceptual research, theoretical modelling, and multidisciplinary studies, I demonstrate how organismal architecture can evolve so that organisms can cope better and better with future environmental challenges. As a first step, I systematically classify the many factors contributing to evolvability. Then I use a simulation approach to show how evolvability-enhancing structures can readily evolve in gene-regulatory networks. This happens via the evolution of "mutational transformers" - structural elements that convert random mutations at the genetic level into adaptation-enhancing mutations at the phenotypic level. In another thesis chapter, I demonstrate that even if selection acts only sporadically, complex adaptations can evolve and persist over long time periods. In other words, complex adaptations do not require constant selection pressure. In an interdisciplinary contribution, I apply biological insights regarding the properties of an evolvability-enhancing mutation structure to the design of algorithms used in Artificial Intelligence. The result is the “Facilitated Mutation” method which enhances the performance of the algorithms in various respects, highlighting the potential for leveraging biological principles in computational sciences. Finally, I embed my research findings in a philosophical context. I emphasise the importance of organismal architecture in retaining evolutionary memories and suggest future research directions to further enhance our understanding of evolvability