Evolution of Heterogeneous Cellular Automata in Fluctuating Environments

The importance of environmental fluctuations in the evolution of living organisms by natural selection has been widely noted by biologists and linked to many important characteristics of life such as modularity, plasticity, genotype size, mutation rate, learning, or epigenetic adaptations. In artificial-life simulations, however, environmental fluctuations are usually seen as a nuisance rather than an essential characteristic of evolution. HetCA is a heterogeneous cellular automata characterized by its ability to generate open-ended long-term evolution and “evolutionary progress”. In this paper, we propose to measure the impact of different types of environmental fluctuations in HetCA. Our results indicate that environmental changes induce mechanisms analogous to epigenetic adaptation or multilevel selection. This is particularly prevalent in two of the tested fluctuation schemes, which involve a round-robin inhibition of certain cell types, where phenotypic selection seems to occur.Funding for this work was provided by the Science Foundation Ireland and the ERC Advanced Grant EPNet #340828. Some of the simulations were run on the MareNostrum supercomputer of the Barcelona Supercomputing Center.Postprint (author's final draft

A New Wave: A Dynamic Approach to Genetic Programming

Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heterogeneity to simulate a dynamic evolutionary environment by incorporating self adaptive parameters together with an innovative approach to population renewal. We conduct an empirical study comparing this new approach with multiple linear regression~(MLR) as well as several evolutionary computation~(EC) methods including the well known geometric semantic genetic programming~(GSGP) together with several other optimised Wave techniques. The results of our investigation show that the dynamic Wave algorithm delivers consistently equal or better performance than Standard GP (both with or without linear scaling), achieves testing fitness equal or better than multiple linear regression, and performs significantly better than GSGP on five of the six problems studied

Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming

Charles Darwin introduced the theory of evolution by natural selection in [Darwin and Wallace, 1858], and since that time the concept has remained unchanged at the highest level. However, there have been numerous and animated debates about its concrete implementation. Evolutionary Computation (EC) has taken inspiration from these debates, for example recently by reusing ideas from epigenetics [Tanev and Yuta, 2003]. We propose here to focus on a point which, although not central to the theory itself, comes up regularly in the polemics that have marked the eld of evolutionary biology: environmental uctuations. That is to say events uctuating randomly or regularly and modifying the optimal strategy maximizing an individual's tness. This thesis studies the e ects of environmental uctuations on natural selection in the context of computer simulations such as Genetic Programming (GP) and \open-ended" arti cial life simulations. The remainder of this chapter is organised as follows: Section 1.1 describes the motivation behind this research; Section 1.2 presents the research questions and the objectives addressed in the thesis; then Section 1.3 lists the contributions; nally Section 1.4 presents the structure of the thesis

Efficient Interleaved Sampling of Training Data in Genetic Programming

The ability to generalize beyond the training set is important for Genetic Programming (GP). Interleaved Sampling is a recently proposed approach to improve generalization in GP. In this technique, GP alternates between using the entire data set and only a single data point. Initial results showed that the technique not only produces solutions that generalize well, but that it so happens at a reduced computational expense as half the number of generations only evaluate a single data point. This paper further investigates the merit of interleaving the use of training set with two alternatives approaches. These are: the use of random search instead of a single data point, and simply minimising the tree size. Both of these alternatives are computationally even cheaper than the original setup as they simply do not invoke the fitness function half the time. We test the utility of these new methods on four, well cited, and high dimensional problems from the symbolic regression domain. The results show that the new approaches continue to produce general solutions despite taking only half the fitness evaluations. Size minimisation also prevents bloat while producing competitive results on both training and test data sets. The tree sizes with size minisation are substantially smaller than the rest of the setups, which further brings down the training costs

Wave: A Heterogeneous Genetic Programming Approach to Divide and Conquer

This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short, and dependent but potentially heterogeneous GP runs provides a collective solution; the sequence akins a wave such that each short GP run is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling

Wave: Incremental Erosion of Residual Error

Typically, Genetic Programming (GP) attempts to solve a problem by evolving solutions over a large, and usually pre-determined number of generations. However, overwhelming evidence shows that not only does the rate of performance improvement drop considerably after a few early generations, but that further improvement also comes at a considerable cost (bloat). Furthermore, each simulation (a GP run), is typically independent yet homogeneous: it does not re-use solutions from a previous run and retains the same experimental settings. Some recent research on symbolic regression divides work across GP runs where the subsequent runs optimise the residuals from a previous run and thus produce a cumulative solution; however, all such subsequent runs (or iterations) still remain homogeneous thus using a pre-set, large number of generations (50 or more). This work introduces Wave, a divide and conquer approach to GP whereby a sequence of short but sharp, and dependent yet potentially heterogeneous GP runs provides a collective solution; the sequence is akin to a wave such that each member of the sequence (that is, a short GP run) is a period of the wave. Heterogeneity across periods results from varying settings of system parameters, such as population size or number of generations, and also by alternating use of the popular GP technique known as linear scaling. The results show that Wave trains faster and better than both standard GP and multiple linear regression, can prolong discovery through constant restarts (which as a side effect also reduces bloat), can innovatively leverage a learning aid, that is, linear scaling at various stages instead of using it constantly regardless of whether it helps and performs reasonably even with a tiny population size (25) which bodes well for real time or data intensive training

Evolution of Heterogeneous Cellular Automata in Fluctuating Environments

The importance of environmental fluctuations in the evolution of living organisms by natural selection has been widely noted by biologists and linked to many important characteristics of life such as modularity, plasticity, genotype size, mutation rate, learning, or epigenetic adaptations. In artificial-life simulations, however, environmental fluctuations are usually seen as a nuisance rather than an essential characteristic of evolution. HetCA is a heterogeneous cellular automata characterized by its ability to generate open-ended long-term evolution and “evolutionary progress”. In this paper, we propose to measure the impact of different types of environmental fluctuations in HetCA. Our results indicate that environmental changes induce mechanisms analogous to epigenetic adaptation or multilevel selection. This is particularly prevalent in two of the tested fluctuation schemes, which involve a round-robin inhibition of certain cell types, where phenotypic selection seems to occur.Funding for this work was provided by the Science Foundation Ireland and the ERC Advanced Grant EPNet #340828. Some of the simulations were run on the MareNostrum supercomputer of the Barcelona Supercomputing Center

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ComputeOps: Container for High Performance Computing

The High Performance Computing (HPC) domain aims to optimize code in order to use the latest multicore and parallel technologies including specific processor instructions. In this computing framework, portability and reproducibility are key concepts. A way to handle these requirements is to use Linux containers. These “light virtual machines” allow to encapsulate applications within its environment in Linux processes. Containers have been recently rediscovered due to their abilities to provide both multi-infrastructure environnement for developers and system administrators and reproducibility due to image building file. Two container solutions are emerging: Docker for microservices and Singularity for computing applications. We present here the status of the ComputeOps project which has the goal to study the benefit of containers for HPC applications