Fitness sharing and niching methods revisited

Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study compares the sharing method with other niching techniques

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Assessing architectural evolution: A case study

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach

Optimal reconstruction of concentrations, gradients and reaction rates from particle distributions

Random walk particle tracking methodologies to simulate solute transport of conservative species constitute an attractive alternative for their computational efficiency and absence of numerical dispersion. Yet, problems stemming from the reconstruction of concentrations from particle distributions have typically prevented its use in reactive transport problems. The numerical problem mainly arises from the need to first reconstruct the concentrations of species/components from a discrete number of particles, which is an error prone process, and then computing a spatial functional of the concentrations and/or its derivatives (either spatial or temporal). Errors are then propagated, so that common strategies to reconstruct this functional require an unfeasible amount of particles when dealing with nonlinear reactive transport problems. In this context, this article presents a methodology to directly reconstruct this functional based on kernel density estimators. The methodology mitigates the error propagation in the evaluation of the functional by avoiding the prior estimation of the actual concentrations of species. The multivariate kernel associated with the corresponding functional depends on the size of the support volume, which defines the area over which a given particle can influence the functional. The shape of the kernel functions and the size of the support volume determines the degree of smoothing, which is optimized to obtain the best unbiased predictor of the functional using an iterative plug-in support volume selector. We applied the methodology to directly reconstruct the reaction rates of a precipitation/dissolution problem involving the mixing of two different waters carrying two aqueous species in chemical equilibrium and moving through a randomly heterogeneous porous mediu

An Evolutionary Perspective of Radical Innovation and its implications for Management and Organizations

The thesis develops an evolutionary perspective of technological change based on a complex analogy between biological and technological evolution. The theoretical framework is based on a rich tradition of interdisciplinary research, integrating Herbert Simon\u2019s seminal theory on modular complex systems, artifact-centered evolutionary models of innovation (e.g. Basalla\u2019s), and fundamental evolutionary processes recently researched in microbiology \u2013 including in particular exaptation and horizontal transfer. The novel evolutionary perspective is supported by analytical narratives of paradigmatic historical and prehistorical cases \u2013 including the bow-and-arrow and the turbojet revolution \u2013 emphasizing its explanatory power in understanding presumptive anomalies and the inception of radical innovation. Finally, some implications for innovation management (managing creative radical engineering), organizations (rethinking the mirror hypothesis) are explored as promising implications of this novel perspective of technological change.The thesis develops an evolutionary perspective of technological change based on a complex analogy between biological and technological evolution. The theoretical framework is based on a rich tradition of interdisciplinary research, integrating Herbert Simon\u2019s seminal theory on modular complex systems, artifact-centered evolutionary models of innovation (e.g. Basalla\u2019s), and fundamental evolutionary processes recently researched in microbiology \u2013 including in particular exaptation and horizontal transfer. The novel evolutionary perspective is supported by analytical narratives of paradigmatic historical and prehistorical cases \u2013 including the bow-and-arrow and the turbojet revolution \u2013 emphasizing its explanatory power in understanding presumptive anomalies and the inception of radical innovation. Finally, some implications for innovation management (managing creative radical engineering), organizations (rethinking the mirror hypothesis) are explored as promising implications of this novel perspective of technological change

Hydrogeochemical Modeling of Saltwater Intrusion and Water Supply Augmentation in South Florida

The Biscayne Aquifer is a primary source of water supply in Southeast Florida. As a coastal aquifer, it is threatened by saltwater intrusion (SWI) when the natural groundwater flow is altered by over-pumping of groundwater. SWI is detrimental to the quality of fresh groundwater sources, making the water unfit for drinking due to mixing and reactions with aquifer minerals. Increasing water demand and complex environmental issues thus force water utilities in South Florida to sustainably manage saltwater intrusion and develop alternative water supplies (e.g., aquifer storage and recovery, ASR). The objectives of this study were to develop and use calibrated geochemical models to estimate water quality changes during saline intrusion and during ASR in south Florida. A batch-reaction model of saltwater intrusion was developed and important geochemical reactions were inferred. Additionally, a reactive transport model was developed to assess fate and transport of major ions and trace metals (Fe, As) at the Kissimmee River ASR. Finally, a cost-effective management of saltwater intrusion that involves using abstraction and recharge wells was implemented and optimized for the case of the Biscayne Aquifer. Major processes in the SWI areas were found to be mixing and dissolution-precipitation reactions with calcite and dolomite. Most of the major ions (Cl, Na, K, Mg, SO4) behaved conservatively during ASR while Ca and alkalinity were affected by carbonate reactions and cation exchange. A complex set of reactions involving thermodynamic equilibrium, kinetics and surface complexation reactions was required in the ASR model to simulate observed concentrations of Fe and As. The saltwater management model aimed at finding optimal locations and flow rates for abstraction and recharge wells. Optimal solutions (i.e., minimum total salt and total cost Pareto front) were produced for the Biscayne Aquifer for scenarios of surface recharge induced by climate change-affected precipitation. In general, abstraction at the maximum rate near the coast and artificial recharge at locations much further inland were found to be optimal. Knowledge developed herein directly supports the understanding of SWI caused by anthropogenic stressors, such as over-pumping and sea level rise, on coastal aquifers

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

Speciation in Behavioral Space for Evolutionary Robotics

International audienceIn Evolutionary Robotics, population-based evolutionary computation is used to design robot neurocontrollers that produce behaviors which allow the robot to fulfill a user-defined task. However, the standard approach is to use canonical evolutionary algorithms, where the search tends to make the evolving population converge towards a single behavioral solution, even if the high-level task could be accomplished by structurally different behaviors. In this work, we present an approach that preserves behavioral diversity within the population in order to produce a diverse set of structurally different behaviors that the robot can use. In order to achieve this, we employ the concept of speciation, where the population is dynamically subdivided into sub-groups, or species, each one characterized by a particular behavioral structure that all individuals within that species share. Speciation is achieved by describing each neurocontroller using a representations that we call a behavior signature, these are descriptors that characterize the traversed path of the robot within the environment. Behavior signatures are coded using character strings, this allows us to compare them using a string similarity measure, and three measures are tested. The proposed behavior-based speciation is compared with canonical evolution and a method that speciates based on network topology. Experimental tests were carried out using two robot tasks (navigation and homing behavior), several training environments, and two different robots (Khepera and Pioneer), both real and simulated. Results indicate that behavior-based speciation increases the diversity of the behaviors based on their structure, without sacrificing performance. Moreover, the evolved controllers exhibit good robustness when the robot is placed within environments that were not used during training. In conclusion, the speciation method presented in this work allows an evolutionary algorithm to produce several robot behaviors that are structurally different but all are able to solve the same robot task