905,923 research outputs found
Natural evolution strategies and variational Monte Carlo
A notion of quantum natural evolution strategies is introduced, which
provides a geometric synthesis of a number of known quantum/classical
algorithms for performing classical black-box optimization. Recent work of
Gomes et al. [2019] on heuristic combinatorial optimization using neural
quantum states is pedagogically reviewed in this context, emphasizing the
connection with natural evolution strategies. The algorithmic framework is
illustrated for approximate combinatorial optimization problems, and a
systematic strategy is found for improving the approximation ratios. In
particular it is found that natural evolution strategies can achieve
approximation ratios competitive with widely used heuristic algorithms for
Max-Cut, at the expense of increased computation time
The world of strategies with memory
As part of a generalized ”prisoners’ dilemma”, is considered that the evolution of a
population with a full set of behavioral strategies limited only by the depth of memory.
Each subsequent generation of the population successively loses the most disadvantageous
strategies of behavior of the previous generation. It is shown that an increase in memory in
a population is evolutionarily beneficial. The winners of evolutionary selection invariably
refer to agents with maximum memory. The concept of strategy complexity is introduced.
It is shown that strategies that win in natural selection have maximum or near maximum
complexity. Despite the fact that at a separate stage of evolution, according to the
payout matrix, the individual gain, while refusing to cooperate, exceeded the gain obtained
while cooperating. The winning strategies always belonged to the so-called respectable
strategies that are clearly prone to cooperation
Natural resources conservation management and strategies in agriculture
This paper suggests a holistic framework for assessment and improvement of management strategies for conservation of natural resources in agriculture. First, it incorporates an interdisciplinary approach (combining Economics, Organization, Law, Sociology, Ecology, Technology, Behavioral and Political Sciences) and presents a modern framework for assessing environmental management and strategies in agriculture including: specification of specific “managerial needs” and spectrum of feasible governance modes (institutional environment; private, collective, market, and public modes) of natural resources conservation at different level of decision-making (individual, farm, eco-system, local, regional, national, transnational, and global); specification of critical socio-economic, natural, technological, behavioral etc. factors of managerial choice, and feasible spectrum of (private, collective, public, international) managerial strategies; assessment of efficiency of diverse management strategies in terms of their potential to protect diverse eco-rights and investments, assure socially desirable level of environmental protection and improvement, minimize overall (implementing, third-party, transaction etc.) costs, coordinate and stimulate eco-activities, meet preferences and reconcile conflicts of individuals etc. Second, it presents evolution and assesses the efficiency of diverse management forms and strategies for conservation of natural resources in Bulgarian agriculture during post-communist transformation and EU integration (institutional, market, private, and public), and evaluates the impacts of EU CAP on environmental sustainability of farms of different juridical type, size, specialization and location. Finally, it suggests recommendations for improvement of public policies, strategies and modes of intervention, and private and collective strategies and actions for effective environmental protection
The Evolution of Extortion in Iterated Prisoner's Dilemma Games
Iterated games are a fundamental component of economic and evolutionary game
theory. They describe situations where two players interact repeatedly and have
the possibility to use conditional strategies that depend on the outcome of
previous interactions. In the context of evolution of cooperation, repeated
games represent the mechanism of reciprocation. Recently a new class of
strategies has been proposed, so called 'zero determinant strategies'. These
strategies enforce a fixed linear relationship between one's own payoff and
that of the other player. A subset of those strategies are 'extortioners' which
ensure that any increase in the own payoff exceeds that of the other player by
a fixed percentage. Here we analyze the evolutionary performance of this new
class of strategies. We show that in reasonably large populations they can act
as catalysts for the evolution of cooperation, similar to tit-for-tat, but they
are not the stable outcome of natural selection. In very small populations,
however, relative payoff differences between two players in a contest matter,
and extortioners hold their ground. Extortion strategies do particularly well
in co-evolutionary arms races between two distinct populations: significantly,
they benefit the population which evolves at the slower rate - an instance of
the so-called Red King effect. This may affect the evolution of interactions
between host species and their endosymbionts.Comment: contains 4 figure
Synthetic Gene Circuits: Design with Directed Evolution
Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward. Synthetic circuits generally consist of components optimized to function in their natural context, not in the context of the synthetic circuit. Combining mathematical modeling with directed evolution offers one promising means for addressing this problem. Modeling identifies mutational targets and limits the evolutionary search space for directed evolution, which alters circuit performance without the need for detailed biophysical information. This review examines strategies for integrating modeling and directed evolution and discusses the utility and limitations of available methods
The evolutionary ecology of interactive synchronism: The illusion of the optimal phenotype
In this article, we discuss some ecological-evolutionary strategies that allow synchronization of organisms, resources, and conditions. Survival and reproduction require synchronization of life cycles of organisms with favourable environmental and ecological features and conditions. This interactive synchronization can occur directly, through pairwise or diffuse co-evolution, or indirectly, for example, as a result of actions of ecosystem engineers and facilitator species. Observations of specific interactions, especially those which have coevolved, may give the false impression that evolution results in optimal genotypes or phenotypes. However, some phenotypes may arise under evolutionary constraints, such as simultaneous evolution of multiple traits, lack of a chain of fit transitional forms leading to an optimal phenotype, or by limits inherent in the process of selection, set by the number of selective deaths and by interference between linked variants. Although there are no optimal phenotypes, optimization models applied to particular species may be useful for a better understanding of the nature of adaptations. The evolution of adaptive strategies results in variable life histories. These strategies can minimize adverse impacts on the fitness of extreme or severe environmental conditions on survival and reproduction, and may include reproductive strategies such as semelparity and iteroparity, or morphological, physiological, or behavioural traits such as diapause, seasonal polyphenism, migration, or bet-hedging. However, natural selection cannot indefinitely maintain intra-population variation, and lack of variation can ultimately extinguish populations
Comparing reactive and memory-one strategies of direct reciprocity
Direct reciprocity is a mechanism for the evolution of cooperation based on
repeated interactions. When individuals meet repeatedly, they can use
conditional strategies to enforce cooperative outcomes that would not be
feasible in one-shot social dilemmas. Direct reciprocity requires that
individuals keep track of their past interactions and find the right response.
However, there are natural bounds on strategic complexity: Humans find it
difficult to remember past interactions accurately, especially over long
timespans. Given these limitations, it is natural to ask how complex strategies
need to be for cooperation to evolve. Here, we study stochastic evolutionary
game dynamics in finite populations to systematically compare the evolutionary
performance of reactive strategies, which only respond to the co-player's
previous move, and memory-one strategies, which take into account the own and
the co-player's previous move. In both cases, we compare deterministic strategy
and stochastic strategy spaces. For reactive strategies and small costs, we
find that stochasticity benefits cooperation, because it allows for
generous-tit-for-tat. For memory one strategies and small costs, we find that
stochasticity does not increase the propensity for cooperation, because the
deterministic rule of win-stay, lose-shift works best. For memory one
strategies and large costs, however, stochasticity can augment cooperation.Comment: 18 pages, 7 figure
Exponential Natural Evolution Strategies
The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES comes with important invariance properties. In this paper, we introduce a number of elegant and efficient improvements of the basic NES algorithm. First, we propose to parameterize the positive definite covariance matrix using the exponential map, which allows the covariance matrix to be updated in a vector space. This new technique makes the algorithm completely invariant under linear transformations of the underlying search space, which was previously achieved only in the limit of small step sizes. Second, we compute all updates in the natural coordinate system, such that the natural gradient coincides with the vanilla gradient. This way we avoid the computation of the inverse Fisher information matrix, which is the main computational bottleneck of the original NES algorithm. Our new algorithm, exponential NES (xNES), is significantly simpler than its predecessors. We show that the various update rules in CMA-ES are closely related to the natural gradient updates of xNES. However, xNES is more principled than CMA-ES, as all the update rules needed for covariance matrix adaptation are derived from a single principle. We empirically assess the performance of the new algorithm on standard benchmark function
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