Organisational Memetics?: Organisational Learning as a Selection Process

Companies are not only systems created and controlled by those who manage them but also self-organising entities that evolve through learning. Whereas an organism is a creation of natural replicators, genes, an organisation can be seen as a product of an alternative replicator, the meme or mental model, acting, like a gene, to preserve itself in an Evolutionary Stable System. The result is an organisation which self organises around a set of unspoken and unwritten rules and assumptions. Biological evolution is stimulated by environmental change and reproductive isolation; the process of punctuated equilibrium. Corporate innovation shows the same pattern. Innovations in products and processes occur in groups isolated from prevailing mental norms. Successful organic strains possess a genetic capability for adaptation. Organisations which wish to foster learning can develop an equivalent, mental capability. Unlike their biological counterparts they can exert conscious choice and puncture the memetic codes that seek to keep them stable; the mental models of individuals, and the strategies, paradigms and unwritten rules at the company level

Coevolving memetic algorithms: A review and progress report

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed. © 2007 IEEE

Sustainable Cooperative Coevolution with a Multi-Armed Bandit

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201

Darwinism, probability and complexity : market-based organizational transformation and change explained through the theories of evolution

The study of transformation and change is one of the most important areas of social science research. This paper synthesizes and critically reviews the emerging traditions in the study of change dynamics. Three mainstream theories of evolution are introduced to explain change: the Darwinian concept of survival of the fittest, the Probability model and the Complexity approach. The literature review provides a basis for development of research questions that search for a more comprehensive understanding of organizational change. The paper concludes by arguing for the development of a complementary research tradition, which combines an evolutionary and organizational analysis of transformation and change