4,734 research outputs found
Prospects for computational steering of evolutionary computation
Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented
WHICH CONCEPTUAL FOUNDATIONS FOR ENVIRONMENTAL POLICIES? AN INSTITUTIONAL AND EVOLUTIONARY FRAMEWORK OF ECONOMIC CHANGE
This paper draws on institutional and evolutionary economics and contributes to an approach to environmental policy which diverges from mainstream prescriptions. The 'socio-technical system' is the core concept: this is a complex made of co-evolving institutions, technologies, markets and actors that fulfils an overall societal need (such as housing, production, mobility, etc.). A systemic and dynamic analysis of those structural changes which are needed to create more sustainable socio-technical systems is provided; actors â and their ability to influence politics and policy â are explicitly taken into consideration. Unsustainable socio-technical systems feature a relevant resistance to change, because they are embedded in the very structure of our society and because of the conservative action of dominant stakeholders; this is why no environmental policy will be effective unless it aims at 'unlocking' our societies from their dominance. But also a constructive side of environmental policy is needed in order to establish new and more sustainable socio-technical systems; consistently, environmental policy is viewed as a combination of actions that can trigger, make viable and align those institutional, technological and economic changes which are needed to reach sustainability. Again, actors (for change) are at the heart of this vision of environmental policy: as subject, because the creation of new and sustainable socio-technical systems is made possible by (coalitions of) actors for change; as object, because environmental policy â to be effective â must actively support the empowerment, legitimation and social networking of such coalitions. A 'chicken and egg' problem remains: who comes first? Actors for change advocating policies for sustainability or policies for sustainability supporting actors for change?
Which conceptual foundations for environmental policies? An institutional and evolutionary framework of economic change
This paper draws on institutional and evolutionary economics and contributes to an approach to environmental policy which diverges from mainstream prescriptions. The 'socio-technical system' is the core concept: this is a complex made of co-evolving institutions, technologies, markets and actors that fulfils an overall societal need (such as housing, production, mobility, etc.). A systemic and dynamic analysis of those structural changes which are needed to create more sustainable socio-technical systems is provided; actors â and their ability to influence politics and policy â are explicitly taken into consideration. Unsustainable socio-technical systems feature a relevant resistance to change, because they are embedded in the very structure of our society and because of the conservative action of dominant stakeholders; this is why no environmental policy will be effective unless it aims at 'unlocking' our societies from their dominance. But also a constructive side of environmental policy is needed in order to establish new and more sustainable socio-technical systems; consistently, environmental policy is viewed as a combination of actions that can trigger, make viable and align those institutional, technological and economic changes which are needed to reach sustainability. Again, actors (for change) are at the heart of this vision of environmental policy: as subject, because the creation of new and sustainable socio-technical systems is made possible by (coalitions of) actors for change; as object, because environmental policy â to be effective â must actively support the empowerment, legitimation and social networking of such coalitions. A âchicken and eggâ problem remains: who comes first? Actors for change advocating policies for sustainability or policies for sustainability supporting actors for change?Environmental policy; Economic dynamics; Institutional economics; Evolutionary economics; Socio-technical systems
Evidence of coevolution in multi-objective evolutionary algorithms
This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of 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
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