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

Discrete stochastic processes, replicator and Fokker-Planck equations of coevolutionary dynamics in finite and infinite populations

Finite-size fluctuations in coevolutionary dynamics arise in models of biological as well as of social and economic systems. This brief tutorial review surveys a systematic approach starting from a stochastic process discrete both in time and state. The limit $N\to \infty$ of an infinite population can be considered explicitly, generally leading to a replicator-type equation in zero order, and to a Fokker-Planck-type equation in first order in $1/\sqrt{N}$. Consequences and relations to some previous approaches are outlined.Comment: Banach Center publications, in pres

Eco-evolutionary dynamics on deformable fitness landscapes

Conventional approaches to modelling ecological dynamics often do not include evolutionary changes in the genetic makeup of component species and, conversely, conventional approaches to modelling evolutionary changes in the genetic makeup of a population often do not include ecological dynamics. But recently there has been considerable interest in understanding the interaction of evolutionary and ecological dynamics as coupled processes. However, in the context of complex multi-species ecosytems, especially where ecological and evolutionary timescales are similar, it is difficult to identify general organising principles that help us understand the structure and behaviour of complex ecosystems. Here we introduce a simple abstraction of coevolutionary interactions in a multi-species ecosystem. We model non-trophic ecological interactions based on a continuous but low-dimensional trait/niche space, where the location of each species in trait space affects the overlap of its resource utilisation with that of other species. The local depletion of available resources creates, in effect, a deformable fitness landscape that governs how the evolution of one species affects the selective pressures on other species. This enables us to study the coevolution of ecological interactions in an intuitive and easily visualisable manner. We observe that this model can exhibit either of the two behavioural modes discussed in the literature; namely, evolutionary stasis or Red Queen dynamics, i.e., continued evolutionary change. We find that which of these modes is observed depends on the lag or latency between the movement of a species in trait space and its effect on available resources. Specifically, if ecological change is nearly instantaneous compared to evolutionary change, stasis results; but conversely, if evolutionary timescales are closer to ecological timescales, such that resource depletion is not instantaneous on evolutionary timescales, then Red Queen dynamics result. We also observe that in the stasis mode, the overall utilisation of resources by the ecosystem is relatively efficient, with diverse species utilising different niches, whereas in the Red Queen mode the organisation of the ecosystem is such that species tend to clump together competing for overlapping resources. These models thereby suggest some basic conditions that influence the organisation of inter-species interactions and the balance of individual and collective adaptation in ecosystems, and likewise they also suggest factors that might be useful in engineering artificial coevolution

Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics

In this paper we review evolutionary economic modelling in relation to environmental policy. We discuss three areas in which evolutionary economic models have a particularly high added value for environmental policy-making: the double externality problem, technological transitions and consumer demand. We explore the possibilities to apply evolutionary economic models in environmental policy assessment, including the opportunities for making policy-making endogenous to environmental innovation. We end with a critical discussion of the challenges that remain.

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

Complex eco-evolutionary dynamics induced by the coevolution of predator–prey movement strategies

The coevolution of predators and prey has been the subject of much empirical and theoretical research that produced intriguing insights into the interplay of ecology and evolution. To allow for mathematical analysis, models of predator–prey coevolution are often coarse-grained, focussing on population-level processes and largely neglecting individual-level behaviour. As selection is acting on individual-level properties, we here present a more mechanistic approach: an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape, where features relevant for ecology (like changes in local densities) and evolution (like differences in survival and reproduction) emerge naturally from interactions between individuals. Our focus is on predator–prey movement behaviour, and we present a new method for implementing evolving movement strategies in an efficient and intuitively appealing manner. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of their movement strategies. Over the generations, the movement strategies evolve, as individuals that successfully survive and reproduce leave their strategy to more descendants. We show that the movement strategies in our model evolve rapidly, thereby inducing characteristic spatial patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by antagonistic coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies within the same population. Although the strategy space of our model is continuous, we often observe the evolution of discrete movement types. We argue that rapid evolution, coexistent movement types, and phase shifts between different ecological regimes are not a peculiarity of our model but a result of more realistic assumptions on eco-evolutionary feedbacks and the number of evolutionary degrees of freedom

Coevolutionary Dynamics of Group Interactions: Coevolving Nonlinear Voter Models

We survey the coevolutionary dynamics of network topology and group interactions in opinion formation, grounded on a coevolving nonlinear voter model. The coevolving nonlinear voter model incorporates two mechanisms: group interactions implemented through nonlinearity in the voter model and network plasticity demonstrated as the rewiring of links to remove connections between nodes in different opinions. We show that the role of group interactions, implemented by the nonlinearity can significantly impact both the dynamical outcomes of nodes' state and the network topology. Additionally, we review several variants of the coevolving nonlinear voter model considering different rewiring mechanisms, noise of flipping nodes' state, and multilayer structures. We portray the various aspects of the coevolving nonlinear voter model as an example of network coevolution driven by group interactions, and finally, present the implications and potential directions for future research.Comment: 8 pages, 2 figure