On steady-state evolutionary algorithms and selective pressure: Why inverse rank-based allocation of reproductive trials is best

We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax, we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard (+1) EA and (+1) RLS with any polynomial population sizes. However, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called TruncatedTwoMax, where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise TwoMax efficiently and effectively escape from local optima of TruncatedTwoMax with high probability. Thus, they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the article by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and multidimensional knapsack problems

On Non-Elitist Evolutionary Algorithms Optimizing Fitness Functions with a Plateau

We consider the expected runtime of non-elitist evolutionary algorithms (EAs), when they are applied to a family of fitness functions with a plateau of second-best fitness in a Hamming ball of radius r around a unique global optimum. On one hand, using the level-based theorems, we obtain polynomial upper bounds on the expected runtime for some modes of non-elitist EA based on unbiased mutation and the bitwise mutation in particular. On the other hand, we show that the EA with fitness proportionate selection is inefficient if the bitwise mutation is used with the standard settings of mutation probability.Comment: 14 pages, accepted for proceedings of Mathematical Optimization Theory and Operations Research (MOTOR 2020). arXiv admin note: text overlap with arXiv:1908.0868

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The ((Formula presented.)) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the ((Formula presented.)) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys

Rethinking Service Systems and Public Policy: A Transformative Refugee Service Experience Framework

The global refugee crisis is a complex humanitarian problem. Service researchers can assist in solving this crisis because refugees are immersed in complex human service systems. Drawing on marketing, sociology, transformative service, and consumer research literature, this study develops a Transformative Refugee Service Experience Framework to enable researchers, service actors, and public policy makers to navigate the challenges faced throughout a refugee’s service journey. The primary dimensions of this framework encompass the spectrum from hostile to hospitable refugee service systems and the resulting suffering or well-being in refugees’ experiences. The authors conceptualize this at three refugee service journey phases (entry, transition, and exit) and at three refugee service system levels (macro, meso, and micro) of analysis. The framework is supported by brief examples from a range of service-related refugee contexts as well as a Web Appendix with additional cases. Moreover, the authors derive a comprehensive research agenda from the framework, with detailed research questions for public policy and (service) marketing researchers. Managerial directions are provided to increase awareness of refugee service problems; stimulate productive interactions; and improve collaboration among public and nonprofit organizations, private service providers, and refugees. Finally, this work provides a vision for creating hospitable refugee service systems

Artificial Immune Systems can find arbitrarily good approximations for the NP-Hard partition problem

Typical Artificial Immune System (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which Evolutionary Algorithms (EAs) struggle to escape. Such behaviour has been shown for artificial example functions such as Jump, Cliff or Trap constructed especially to show difficulties that EAs may encounter during the optimisation process. However, no evidence is available indicating that similar effects may also occur in more realistic problems. In this paper we perform an analysis for the standard NP-Hard Partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time. As a result we prove that while EAs and Random Local Search may get trapped on 4/3 approximations, AISs find arbitrarily good approximate solutions of ratio ( 1+ϵ ) for any constant ϵ within a time that is polynomial in the problem size and exponential only in 1/ϵ

An intersectionality framework for transformative services research

Due to copyright restrictions, the access to the full text of this article is only available via subscription.The authors introduce the theory of intersectionality which refers to the interactivity of social identities such as race, class, and gender in shaping individuals’ experiences. Intersectionality is explored using cases and examples from healthcare services, which involve high contact encounters with consumers who may possess multiple disadvantages (e.g. low income, illness, immigrant status) and therefore make for interesting contexts for intersectional analyses. Intersectionality is proposed as a framework that can shed light on the experiences of consumers who belong to multiple disadvantaged social groups, such as being black and low income, immigrant, and in poor health. Detailed guidelines for conducting intersectionality-driven services research are provided, which take into account the interconnected nature of multiple disadvantages. The authors emphasize that intersectionality offers a holistic look at the co-created nature of services and it can be instrumental in designing tailored and fair services to improve consumer and societal well-being

Poverty and intersectionality: a multidimensional look into the lives of the impoverished

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Subsistence consumers are disadvantaged and marginalized on many levels, including financial deprivation, poor health, lack of access to resources, and social stigmatization. The disadvantages experienced by subsistence consumers are interconnected and co-constitutive; being disadvantaged in one domain often intersects with other disadvantages, contributing to an overall vulnerability within the market system. Drawing from the intersectionality paradigm, the authors examine an overlooked low-income community that shares elements of subsistence contexts. The findings reveal multiple ways in which a trailer park community residents experience and manage intertwined disadvantages. Several overlapping identity categories (i.e., socio-economic status, health status, and type of housing) vis-à-vis structural and relational dynamics are fleshed out. Implications for research on subsistence marketplaces and the usefulness of the intersectionality approach for macromarketing research are discussed.ACR/Sheth Foundation Dissertation Grant Award ; AMA Marketing and Society Dissertation Awar

Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift

International audienceA decent number of lower bounds for non-elitist population-based evolutionary algorithms has been shown by now. Most of them are technically demanding due to the (hard to avoid) use of negative drift theorems -- general results which translate an expected movement away from the target into a high hitting time. We propose a simple negative drift theorem for multiplicative drift scenarios and show that it can simplify existing analyses. We discuss in more detail Lehre's (PPSN 2010) \emph{negative drift in populations} method, one of the most general tools to prove lower bounds on the runtime of non-elitist mutation-based evolutionary algorithms for discrete search spaces. Together with other arguments, we obtain an alternative and simpler proof of this result, which also strengthens and simplifies this method. In particular, now only three of the five technical conditions of the previous result have to be verified. The lower bounds we obtain are explicit instead of only asymptotic. This allows to compute concrete lower bounds for concrete algorithms, but also enables us to show that super-polynomial runtimes appear already when the reproduction rate is only a $(1 - \omega(n^{-1/2}))$ factor below the threshold. For the special case of algorithms using standard bit mutation with a random mutation rate (called uniform mixing in the language of hyper-heuristics), we prove the result stated by Dang and Lehre (PPSN 2016) and extend it to mutation rates other than $\Theta(1/n)$, which includes the heavy-tailed mutation operator proposed by Doerr, Le, Makhmara, and Nguyen (GECCO 2017). We finally use our method and a novel domination argument to show an exponential lower bound for the runtime of the mutation-only simple genetic algorithm on \onemax for arbitrary population size