53 research outputs found
Novelty Search in Competitive Coevolution
One of the main motivations for the use of competitive coevolution systems is
their ability to capitalise on arms races between competing species to evolve
increasingly sophisticated solutions. Such arms races can, however, be hard to
sustain, and it has been shown that the competing species often converge
prematurely to certain classes of behaviours. In this paper, we investigate if
and how novelty search, an evolutionary technique driven by behavioural
novelty, can overcome convergence in coevolution. We propose three methods for
applying novelty search to coevolutionary systems with two species: (i) score
both populations according to behavioural novelty; (ii) score one population
according to novelty, and the other according to fitness; and (iii) score both
populations with a combination of novelty and fitness. We evaluate the methods
in a predator-prey pursuit task. Our results show that novelty-based approaches
can evolve a significantly more diverse set of solutions, when compared to
traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem
Solving from Nature (PPSN 2014
Describing coevolution of business and IS alignment via agent-based modeling
The coevolution of business and IS alignment is a growing concern for researchers and practitioners alike. Extant literature on describing and modeling the coevolution is still in infancy, which makes it hard to capture the complexity and to offer reasonable decisions in the evolution of organizations. This paper focuses on the actors’ behaviors, and explores their emergent effects on the holistic alignment. We build an agent-based model to describe the complex alignment landscape and to improve the coevolution governance. The model embraces the emergent behaviors shaped by the interactions of business and IS agents, and guides the coevolution of alignment driven by the external changes. The development of this model forms a necessary step towards suggesting guidance how to analyze and implement coevolution in companies. The paper also shows the capability of an agent-based model to capture some of the emergent behaviors that emerge from bottom-level behaviors
Coevolution, agricultural practices and sustainability: some major social and ecological issues.
Outlines major social and ecological issues involved in the coevolution of social and ecological systems by initially reviewing relevant aspects of the recent literature relating to economic development and their implications for agricultural development. Coevolutionary qualitative- type models are presented. There has been a failure amongst advocates of structural adjustment policies (involving the extension of markets and economic globalisation) to take account of coevolutionary principles and allow for historical differences in the evolution of communities and their varied circumstances. This lack of sensitivity has had unfortunate social and ecological consequences for some communities eg The Russian Federation and subsistence agriculturalists in some less developed countries. The evolution of globalized market systems involving industrial/commercial agriculture (largely dependent on inputs external to the farm) under the 'patronage' of oligopolistic suppliers is seen to increasingly threaten the balance between social and ecological systems and as undermining the sustainabiltiy of both. Capitalistic processes of technological change eg advances in biotechnology, play a major role in this evolution
Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as
instability and mode collapse. These pathologies mainly arise from a lack of
diversity in their adversarial interactions. Evolutionary generative
adversarial networks apply the principles of evolutionary computation to
mitigate these problems. We hybridize two of these approaches that promote
training diversity. One, E-GAN, at each batch, injects mutation diversity by
training the (replicated) generator with three independent objective functions
then selecting the resulting best performing generator for the next batch. The
other, Lipizzaner, injects population diversity by training a two-dimensional
grid of GANs with a distributed evolutionary algorithm that includes neighbor
exchanges of additional training adversaries, performance based selection and
population-based hyper-parameter tuning. We propose to combine mutation and
population approaches to diversity improvement. We contribute a superior
evolutionary GANs training method, Mustangs, that eliminates the single loss
function used across Lipizzaner's grid. Instead, each training round, a loss
function is selected with equal probability, from among the three E-GAN uses.
Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate
that Mustangs provides a statistically faster training method resulting in more
accurate networks
Approximating n-player behavioural strategy nash equilibria using coevolution
Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash. Copyright 2011 ACM
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track record
across a variety of domains from reinforcement learning to artificial life, it
is rarely applied to large, deep neural networks. A central reason is that
while random mutation generally works in low dimensions, a random perturbation
of thousands or millions of weights is likely to break existing functionality,
providing no learning signal even if some individual weight changes were
beneficial. This paper proposes a solution by introducing a family of safe
mutation (SM) operators that aim within the mutation operator itself to find a
degree of change that does not alter network behavior too much, but still
facilitates exploration. Importantly, these SM operators do not require any
additional interactions with the environment. The most effective SM variant
capitalizes on the intriguing opportunity to scale the degree of mutation of
each individual weight according to the sensitivity of the network's outputs to
that weight, which requires computing the gradient of outputs with respect to
the weights (instead of the gradient of error, as in conventional deep
learning). This safe mutation through gradients (SM-G) operator dramatically
increases the ability of a simple genetic algorithm-based neuroevolution method
to find solutions in high-dimensional domains that require deep and/or
recurrent neural networks (which tend to be particularly brittle to mutation),
including domains that require processing raw pixels. By improving our ability
to evolve deep neural networks, this new safer approach to mutation expands the
scope of domains amenable to neuroevolution
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that
evaluate pairs of agents, for example labeling them `winner' and `loser'. If
the game is approximately transitive, then self-play generates sequences of
agents of increasing strength. However, nontransitive games, such as
rock-paper-scissors, can exhibit strategic cycles, and there is no longer a
clear objective -- we want agents to increase in strength, but against whom is
unclear. In this paper, we introduce a geometric framework for formulating
agent objectives in zero-sum games, in order to construct adaptive sequences of
objectives that yield open-ended learning. The framework allows us to reason
about population performance in nontransitive games, and enables the
development of a new algorithm (rectified Nash response, PSRO_rN) that uses
game-theoretic niching to construct diverse populations of effective agents,
producing a stronger set of agents than existing algorithms. We apply PSRO_rN
to two highly nontransitive resource allocation games and find that PSRO_rN
consistently outperforms the existing alternatives.Comment: ICML 2019, final versio
Sorting Networks Design Using Coevolutionary CGP
Tato práce se zabývá návrhem řadicích sítí pomocí kartézskeho genetického programovaní s využitím koevoluce. Řadicí sítě jsou abstraktní modely schopné seřadit posloupnost čísel. Výhodou řadicích sítí je snadná implementovatelnost do hardware, ale jejich návrh je velmi složitý. Jednou z nekonvečních a efektivních možností jak navrhovat řadicí sítě je pomocí kartézskeho genetického programování (CGP). CGP je algoritmus patřící do skupiny evolučních algoritmů inspirovaných Darwinovou evoluční teorii. Efektivitu CGP algoritmu je možno zvýšit použitím koevoluce. Koevoluce je přístup, který pracuje s více populacemi, které se vzájemně ovlivňují a neustále vyvíjejí, čímž zabraňují uváznutí prohledávání v lokálním optimu. V práci je ukázané, že pomocou koevolúcie je možné dosiahnuť takmer dvojnásobné urýchlenie v porovnaní s evolučným návrhom.This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. Sorting networks are abstract models capable of sorting lists of numbers. Advantage of sorting networks is that they are easily implemented in hardware, but their design is very complex. One of the unconventional and effective ways to design sorting networks is Cartesian Genetic Programming (CGP). CGP is one of evolutionary algorithms that are inspired by Darwinian theory of evolution. Efficiency of the CGP algorithm can be increased by using coevolution. Coevolution is an approach that works with multiple populations, which are influencing one another and constantly evolving, thus prevent the local optima deadlock. In this work it is shown, that with the use of coevolution, it is possible to achieve nearly twice the acceleration compared to evolutionary design.
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