Multi-scenario, multi-objective optimization using evolutionary algorithms: Initial results

Deb, Kalyanmoy; Zhu, Ling; Kulkarni, Sandeep S.

doi:10.1109/cec.2015.7257115

Cited by 51 publications

(78 citation statements)

References 9 publications

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“…Multi-objective EA While mono-objective EAs aim to find the optimal solution of a unique function, multi-objective EAs are designed to generate a set of optimal trade-offs between several objectives [46]. Trade-offs are optimal with respect to ordering relations specifically designed for multi-objective spaces, often the Pareto dominance relation, defined as follows: This dominance relation is not a strict ordering.…”

Section: Mono-objective Eamentioning

confidence: 99%

“…For instance, behavioral diversity [130,129,131] is a process helper: the diversity of the population is encouraged by adding an objective [46] that rewards the originality of each behavior with regard to the current population; such a diversity preservation aims at avoiding the premature convergence of the EA, that is, at improving the performance of the evolutionary process. This approach does not change the optimum of the fitness function because the diversity objective is discarded at the end of the evolutionary process and, as a result, final solutions are only ranked by their fitness value.…”

Section: Definition 42 (Process Helper) a Process Helper Intends To mentioning

confidence: 99%

“…[46]) have demonstrated that ranking candidate solutions can be achieved in several ways, and not only by using a single fitness value for each individual. These algorithms have allowed researchers to stop tuning weights of complex, aggregated fitness functions and thus to focus on the content of the objectives [46]. They have also paved the way to helper objectives, which are adjunct objectives used to improve the performance of an evolutionary process [92,90].…”

Section: Introductionmentioning

confidence: 99%

“…First, multi-objective evolutionary algorithms (see, e.g. [46]) have demonstrated that ranking candidate solutions can be achieved in several ways, and not only by using a single fitness value for each individual. These algorithms have allowed researchers to stop tuning weights of complex, aggregated fitness functions and thus to focus on the content of the objectives [46].…”

Section: Introductionmentioning

confidence: 99%

“…[46,56,43]) provide the algorithmic foundation of evolutionary robotics. In their modern form, these population-based optimization algorithms are composed of four components: a genotype, a genotype to phenotype mapping, a set of variation operators, and a user-defined function to be optimized, called a fitness function.…”

Section: Introductionmentioning

confidence: 99%

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Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two subcategories are further considered: task-specific approaches, which require knowledge on how to solve the task and taskagnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

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Section: Mono-objective Eamentioning

confidence: 99%

Section: Definition 42 (Process Helper) a Process Helper Intends To mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

et al. 2013

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[1] Implementation of nonpoint source (NPS) pollution control strategies at the watershed scale hinges on abating pollutant movement from the landscape to water bodies at minimum cost. This paper presents an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices at the watershed scale. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was coupled with a distributed watershed model, Soil and Water Assessment Tool (SWAT), to identify optimal types and locations of conservation practices for nutrient and pesticide control at the watershed scale. Previous optimization studies have used binary representation of nonpoint source pollution controls, even though many could be better characterized as continuous variables. In this study, a novel discrete-continuous decision variable, also known as mixed-variable, representation was used to enhance the versatility of the approach by evaluating more options during the search process. Application of the proposed framework in the Eagle Creek Watershed, Indiana, indicated that the optimal suite of conservation practices from the mixed-variable NSGA-II was more effective in meeting water quality targets at lower costs than the solution from binary-variable optimization. However, the mixed-variable approach was considerably more computationally demanding for assessing tradeoffs between environmental and economic factors. A method for hybridization of binary and mixed-variable NSGA-II methods in the context of nonpoint source pollution control practices was developed to enhance the computational efficiency of the optimization procedure. As a result, the number of model simulations required for convergence to the Pareto-optimal solutions was reduced by 96%. The conceptual complexity and computational requirements of optimization-based approaches are impediments to their wider application for targeted implementation of NPS pollution control strategies. The methods and finding of this study address these issues and could result in a more effective implementation of management strategies at the watershed scale. Hoag, and B. A. Engel (2013), A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices, Water Resour. Res., 49,[8344][8345][8346][8347][8348][8349][8350][8351][8352][8353][8354][8355][8356]

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Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario‐Based Optimization

Sankary

Ostfeld

2018

Water Resources Research

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This paper focuses on evaluating a scenario‐based multiobjective evolutionary algorithm for real‐world design problems in which the environment where a system will operate is dynamic, and uncertain. Subsequently, the performance of a stochastic scenario selection scheme, inspired by methods to reduce overfitting in genetic programming, is investigated for scenario‐based optimization. Using a scenario‐based scheme to address uncertainty in a real‐world system's operational environment, system designs are developed via aggregating the performance of a solution evaluated across many scenarios. Within each generation of the evolutionary algorithm the evaluation suite is resampled and evaluated by the current generation's solutions. This scheme is evaluated on two historical noisy test problems and two real‐world water resources design problem instances. For each case, the stochastic scenario selection scheme is compared to a static selection scheme at various evaluation suite sizes. Results show the proposed scenario selection scheme to outperform static sampling schemes and increase efficiency of a multiobjective evolutionary algorithm for robust optimization objectives.

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Multi-scenario, multi-objective optimization using evolutionary algorithms: Initial results

Cited by 51 publications

References 9 publications

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

Stochastic Scenario Evaluation in Evolutionary Algorithms Used for Robust Scenario‐Based Optimization

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