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

Evolutionary computation for digital art

PowerPoint presentationAneta Neumann, Frank Neuman

Feature selection and novelty in computational aesthetics

[Abstract] An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.Portugal. Fundação para a Ciência e a Tecnologia; PTDC/EIA–EIA/115667/2009Galicia.Consellería de Innovación, Industria e Comercio ; PGIDIT10TIC105008P

Evolutionary design of soft-bodied animats with decentralized control

We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat’s morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment

Standardized outcome measures for pregnancy and childbirth, an ICHOM proposal

Background: Value-based health care aims to optimize the balance of patient outcomes and health care costs. To improve value in perinatal care using this strategy, standard outcomes must first be defined. The objective of this work was to define a minimum, internationally appropriate set of outcome measures for evaluating and improving perinatal care with a focus on outcomes that matter to women and their families. Methods: An interdisciplinary and international Working Group was assembled. Existing literature and current measurement initiatives were reviewed. Serial guided discussions and validation surveys provided consumer input. A series of nine teleconferences, incorporating a modified Delphi process, were held to reach consensus on the proposed Standard Set. Results: The Working Group selected 24 outcome measures to evaluate care during pregnancy and up to 6 months postpartum. These include clinical outcomes such as maternal and neonatal mortality and morbidity, stillbirth, preterm birth, birth injury and patient-reported outcome measures (PROMs) that assess health-related quality of life (HRQoL), mental health, mother-infant bonding, confidence and success with breastfeeding, incontinence, and satisfaction with care and birth experience. To support analysis of these outcome measures, pertinent baseline characteristics and risk factor metrics were also defined. Conclusions: We propose a set of outcome measures for evaluating the care that women and infants receive during pregnancy and the postpartum period. While validation and refinement via pilot implementation projects are needed, we view this as an important initial step towards value-based improvements in care

Bias-variance decomposition in genetic programming

We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set used. We confirm and quantify several insights into the practical usage of GP, most notably that (a) the variance between runs is primarily due to initialization rather than the selection of training samples, (b) parameters can be reasonably optimized to obtain gains in efficacy, and (c) functions detrimental to evolvability are easily eliminated, while functions well-suited to the problem can greatly improve performance—therefore, larger and more diverse function sets are always preferable

Bluenome: A novel developmental model of artificial morphogenesis

Abstract. The Bluenome Model of Development is introduced. The Bluenome model is a developmental model of Artificial Morphogenesis, inspired by biological development, instantiating a subset of two-dimensional Cellular Automata. The Bluenome model is cast as a general model, one which generates organizational topologies for finite sets of component types, assuming only local interactions between components. Its key feature is that there exists no relation between genotypic complexity and phenotypic complexity, implying its potential application in high-dimensional evolutionary problems. Additionally, genomes from the Bluenome Model are shown to be capable of re-development in differing environments, retaining many relevant phenotypic properties.

N.: Environment as a spatial constraint on the growth of structural form

We explore the use of the developmental environment as a spatial constraint on a model of Artificial Embryogeny, applied to the growth of structural forms. A Deva model is used to translate genotype to phenotype, allowing a Genetic Algorithm to evolve Plane Trusses. Genomes are expressed in one of several developmental environments, and selected using a fitness function favouring stability, height, and distribution of pressure. Positive results are found in nearly all cases, demonstrating that environment can be used as an effective spatial constraint on development. Further experiments take genomes evolved in some environment and transplant them into different environments, or re-grow them at different phenotypic sizes; It is shown that while some genomes are highly specialized for the particular environment in which they evolved, others may be re-used in a different context without significant re-design, retaining the majority of their original utility. This strengthens the notion that growth via Artificial Embryogeny can be resistant to perturbations in environment, and that good designs may be re-used in a variety of contexts