When the red harvester ant is threatened, nestmates rush to its assistance. The message is a chemical one. Distress is communicated and help summoned by the potential victim ejecting a tiny cocktail of alkanes and terpenoids. Professor E 0 Wilson, who made this discovery, is a distinguished zoologist whose writings have twice been awarded Pulitzer Prizes. His interests extend far beyond entomology, however. Why should human communication be qualitatively different from that of the ant, in being achieved by definable, physicochemical processes? And, if this is the case, surely the same must be true of other social phenomena? Having conceded so much, on what grounds can we exclude other products of human activity such as the social sciences, arts, humanities and ethics? In a mechanistic universe, the whole of human civilization is the ultimate product of the interaction between genes and environment, incredibly complex, as Wilson admits, but susceptible to fundamentally the same analytic techniques as communication in the harvester ant. Consilience (literally, a 'jumping together') is the notion that a common groundwork of explanation applies not only to the sciences but also to the arts, ethics and religion. In his book Consiliencel Wilson applies a combination of broad learning and a style of baroque exuberance to put the case for such a unity of knowledge. Wilson is thus, as he makes clear, a child of the Enlightenment, paying homage to Bacon and Newton but most of all to Condorcet, who trod the same intellectual path (and who came to grief in the Revolution for reasons probably not unrelated to his philosophy). He mourns the triumph at the end of the eighteenth century of revelatory religion fearful of the outcomes of science, and most of all of Romanticism although regrettably overlooking the

Consilience: The Unity of Knowledge.

The vehicle routing problem

Memetic computing is a subject in computer science which considers complex structures such as the combination of simple agents and memes, whose evolutionary interactions lead to intelligent complexes capable of problem-solving. The founding cornerstone of this subject has been the concept of memetic algorithms, that is a class of optimization algorithms whose structure is characterized by an evolutionary framework and a list of local search components. This article presents a broad literature review on this subject focused on optimization problems. Several classes of optimization problems, such as discrete, continuous, constrained, multi-objective and characterized by uncertainties, are addressed by indicating the memetic “recipes” proposed in the literature. In addition, this article focuses on implementation aspects and especially the coordination of memes which is the most important and characterizing aspect of a memetic structure. Finally, some considerations about future trends in the subject are given.

Memetic algorithms and memetic computing optimization: A literature review

The design of evolutionary algorithms has typically been focused on efficiently solving a single optimization problem at a time. Despite the implicit parallelism of population-based search, no attempt has yet been made to multitask, i.e., to solve multiple optimization problems simultaneously using a single population of evolving individuals. Accordingly, this paper introduces evolutionary multitasking as a new paradigm in the field of optimization and evolutionary computation. We first formalize the concept of evolutionary multitasking and then propose an algorithm to handle such problems. The methodology is inspired by biocultural models of multifactorial inheritance , which explain the transmission of complex developmental traits to offspring through the interactions of genetic and cultural factors. Furthermore, we develop a cross-domain optimization platform that allows one to solve diverse problems concurrently. The numerical experiments reveal several potential advantages of implicit genetic transfer in a multitasking environment. Most notably, we discover that the creation and transfer of refined genetic material can often lead to accelerated convergence for a variety of complex optimization functions.

Multifactorial Evolution: Toward Evolutionary Multitasking

Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this paper, a comprehensive multi-facet survey of recent research in memetic computation is presented.

A Multi-Facet Survey on Memetic Computation

Taking a lead from the multi-faceted definitions and roles of the term "meme" in memetics, a plethora of potentially rich memetic computing methodologies, frameworks and operational memeinspired algorithms have been developed with considerable success in several realworld domains in the last two decades. This article showcase several successful deployments of memetic computing methodologies for solving complex problems, from science, engineering to digital arts.

/pdf/memetic-computation-past-present-future-research-frontier-23w65azdyl.pdf

Memetic Computation—Past, Present & Future [Research Frontier]

The general problem of path planning can be modeled as a traveling salesman problem which assumes that a graph is fully connected. Such a scenario of full connectivity is however not always realistic. One such motivating example for us is the application of path planning for unmanned reconnaissance aerial vehicles (URAVs). URAVs are widely deployed for photography or imagery gathering missions of sites of interest. These sites can be targets in a combat zone to be investigated or sites inaccessible by ground transportation, such as those hit by forest fires, earthquake or other forms of natural disasters. The navigation environment is one where the overall configuration of the problem is a sparse graph. Unlike graphs that are fully connected, sparse graphs are not always Hamiltonian. In this paper, we describe hybrid ant colony algorithms (HACAs) proposed for path planning in sparse graphs since existing ant colony solvers designed for solving TSP do not apply to the present context directly. HACAs represent ant inspired algorithms incorporated with a local search procedure and some heuristic techniques for uncovering feasible route(s) or path(s) in a sparse graph within tractable time. Empirical results conducted on a set of generated sparse graphs demonstrate the excellent convergence property and robustness of HACAs in uncovering low risk and Hamiltonian visitation paths. Further, the obtained results also indicate that HACAs converge to secondary closed paths in situations where a Hamiltonian cycle does not exist theoretically or is not attainable within the bounded computational time window.

Hybrid ant colony algorithms for path planning in sparse graphs

Due to the complex nature and numerous interacting factors that contributes to the increased vulnerability of supply chains, traditional methods have been found to be inadequate for Supply Chain Risk Management (SCRM). Agent-Based Modeling and Simulation (ABMS), an agent-oriented approach to model and simulate complex adaptive systems, represents a recent development in supply chain planning that has been regarded highly appropriate for studying risk management [1-4]. The objective of this paper is to provide a multi-perspective survey of the state-of-the-art agent based modeling and simulation approaches for SCRM.

Agent-Based Modeling and Simulation for Supply Chain Risk Management - A Survey of the State-of-the-Art

In science, gene provides the instruction for making proteins, while meme is the sociocultural equivalent of a gene containing instructions for carrying out behavior. Taking inspiration from nature, we model the memeplex in search as instructions that specify the coadapted meme complexes of individuals in their lifetime. In particular, this paper presents a study on the conceptual modeling of meme complexes or memeplexes for more effective problem solving in the context of modern stochastic optimization. The memeplex representation, credit assignment criteria for meme coadaptation, and the role of emergent memeplexes in the lifetime learning process of a memetic algorithm in search are presented. A coadapted memetic algorithm that takes the proposed conceptual modeling of memeplexes into actions to solve capacitated vehicle routing problems (CVRPs) of diverse characteristics is then designed. Results showed that adaptive memeplexes provide a means of creating highly robust, self-configuring, and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and metaheuristics, on a plethora of CVRP sets considered.

Xianshun Chen

Papers

A Multi-Facet Survey on Memetic Computation

Memetic Computation—Past, Present & Future [Research Frontier]

Hybrid ant colony algorithms for path planning in sparse graphs

Agent-Based Modeling and Simulation for Supply Chain Risk Management - A Survey of the State-of-the-Art

A Conceptual Modeling of Meme Complexes in Stochastic Search