The N-strikes-out algorithm: A steady-state algorithm for coevolution

We introduce the N-strikes-out algorithm, a simple steady-state genetic algorithm for competitive coevolution. The algorithm can be summarised as follows: Run competitions between randomly chosen individuals, keep track of the number of defeats for each individual, and remove any individual which has been defeated N times. Naive application of the algorithm in 2-population problems leads to severe disengagement. We find that disengagement can be eliminated (for all tasks involving real-valued continuous scores) by determining 'victories' and 'defeats' between fellow members of the same species, using competitions against a single member of the opposing species as a point of comparison. We apply our algorithm to the "box-grabbing" problem for artificial 3D creatures introduced by Sims. We compare our algorithm with Sims' original Last Elite Opponent algorithm, and describe (and explain) different results obtained with two different implementations differing mainly by the harshness of their selection regimes

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis

Evolutionary improvement of programs

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks

Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to train, and several aspects of the model should be previously designed by hand. Neuroevolution is a well-known technique used to provide the automatic design of network architectures which was recently expanded to deep neural networks. COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures. COEGAN makes use of the adversarial aspect of the GAN components to implement coevolutionary strategies in the training algorithm. Our proposal was evaluated in the Fashion-MNIST and MNIST dataset. We compare our results with a baseline based on DCGAN and also with results from a random search algorithm. We show that our method is able to discover efficient architectures in the Fashion-MNIST and MNIST datasets. The results also suggest that COEGAN can be used as a training algorithm for GANs to avoid common issues, such as the mode collapse problem.Comment: Published in GECCO 2019. arXiv admin note: text overlap with arXiv:1912.0617

Extending Astrobiology: Consciousness and Culture

The Stanley Miller experiment suggests that amino acid-based life is ubiquitous in our universe, although its varieties are not likely to have followed the particular, highly contingent and path-dependent, evolutionary trajectory found on Earth. Are many alien organisms likely to be conscious in ways that we would recognize? Almost certainly. Will some develop high order technology? Less likely, but still fairly probable. If so, will we be able to communicate with them? Only on a basic level, and only with profound difficulty. The argument is fairly direct

A Coevolutionary Journey of Strategic Knowledge Management Alignment: A Chinese Case

Although knowledge has emerged as the strategic resource of the firm in the increasingly turbulent and dynamic environment, it is underestimated how knowledge management (KM) contributes to sustained competitive advantage of the firm over time. Drawing upon a coevolutionary view of alignment, this study examines a strategic KM coevolutionary mechanism in which KM strategy, processes, and infrastructure dynamically align with the changing competitive strategy; in turn, the KM derived competitive advantage drives the firm to pursue a more superior position in its niche. To trace the coevolutionary mechanism, we conducted a case study in Li-Ning Company, which experiences 20 years’ development and has become a leading sports goods company in China. Two strategic transitions result in the corresponding changes of its KM strategy, KM processes and infrastructure. The cumulated knowledge helps the firm upgrade from an imitator to a prospector with balanced performance portfolio. Theoretical and managerial implications are discussed