"Lose 30lbs in 30 days" : assigning responsibility for deceptive advertising of weight-loss products

Purpose &ndash; The aim of this paper is to outline key social marketing issues apparent in deceptive weight-loss advertising, from the perspective of government policy-makers, manufacturers, the media, and consumers. The purpose is to examine the complexity of one aspect of the obesity battle and provide a framework for coordinated and integrated social marketing initiatives from a multiple stakeholder perspective.Design/methodology/approach &ndash; The results of deceptive weight-loss advertising are framed using the harm chain model, and the paper offers recommended solutions based on a framework of marketing, education and policy changes across the network of stakeholders.Findings &ndash; This paper concludes that a resolution to the harm created by deceptive weight-loss advertising can be achieved by the creation of a more holistic, system-wide solution to this important health and policy issue. This networked approach must involve all aspects of harm in a multi-stakeholder solution, including both upstream and downstream integration. Specific recommendations are made for policy-makers, manufacturers, the media, and consumers to achieve this goal.Social implications &ndash; From a marketing perspective, analyzing the issue of deceptive weight-loss advertising using the harm chain allows for the creation of a more holistic, system-wide solution involving stakeholders in all aspects of harm for this important health and policy issue.Originality/value &ndash; This research examines the problem of obesity and weight-loss advertising from the unique perspective of the harm chain framework. The authors make unified recommendations for various stakeholders including industry, media, government and consumers, in order to direct integrated social marketing and consumer-oriented strategies within this industry.<br /

Leveraging Human Insights by Combining Multi-Objective Optimization with Interactive Evolution

Deceptive fitness landscapes are a growing concern for evolutionary computation. Recent work has shown that combining human insights with short-term evolution has a synergistic effect that accelerates the discovery of solutions. While humans provide rich insights, they fatigue easily. Previous work reduced the number of human evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, this approach lacks the ability to measure what the human evaluator identifies as important. The key insight here is that multi-objective evolutionary algorithms foster diversity, serving as a surrogate for novelty, while measuring user preferences. This approach, called Pareto Optimality-Assisted Interactive Evolutionary Computation (POA-IEC), allows users to identify candidates that they feel are promising. Experimental results reveal that POA-IEC finds solutions in fewer evaluations than previous approaches, and that the non-dominated set is significantly more novel than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences

Devising effective novelty search algorithms: A comprehensive empirical study

Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It is still unclear, however, how some key parameters and algorithmic components influence the evolutionary dynamics and performance of novelty search. In this paper, we conduct a comprehensive empirical study focused on novelty search’s algorithmic components. We study the k parameter — the number of nearest neighbours used in the computation of novelty scores; the use and function of an archive; how to combine novelty search with fitness-based evolution; and how to configure the mutation rate of the underlying evolutionary algorithm. Our study is conducted in a simulated maze navigation task. Our results show that the configuration of novelty search can have a significant impact on performance and behaviour space exploration. We conclude with a number of guidelines for the implementation and configuration of novelty search, which should help future practitioners to apply novelty search more effectively.info:eu-repo/semantics/acceptedVersio

Mobile transporter path planning

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research

Fusing novelty and surprise for evolving robot morphologies

Traditional evolutionary algorithms tend to converge to a single good solution, which can limit their chance of discovering more diverse and creative outcomes. Divergent search, on the other hand, aims to counter convergence to local optima by avoiding selection pressure towards the objective. Forms of divergent search such as novelty or surprise search have proven to be beneficial for both the efficiency and the variety of the solutions obtained in deceptive tasks. Importantly for this paper, early results in maze navigation have shown that combining novelty and surprise search yields an even more effective search strategy due to their orthogonal nature. Motivated by the largely unexplored potential of coupling novelty and surprise as a search strategy, in this paper we investigate how fusing the two can affect the evolution of soft robot morphologies. We test the capacity of the combined search strategy against objective, novelty, and surprise search, by comparing their efficiency and robustness, and the variety of robots they evolve. Our key results demonstrate that novelty-surprise search is generally more efficient and robust across eight different resolutions. Further, surprise search explores the space of robot morphologies more broadly than any other algorithm examined.peer-reviewe