Evolutionary diversity optimization using multi-objective indicators

Neumann, Aneta; Gao, Wanru; Wagner, Markus; Neumann, Franz‐Josef

doi:10.1145/3321707.3321796

Cited by 44 publications

(25 citation statements)

References 26 publications

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“…Finally, we currently rely on irace's built-in exploration of the generator configuration space to ensure the diversity of the generated instances. This can be improved by investigating more advanced approaches for controlling instance diversity more directly, including incorporating a diversity measurement such as multi-objective indicators [25] into the scoring values of the tuning, or forcing each generator configuration to generate instances far away from the current instance set by adding constraints into the generator model.…”

Section: Discussionmentioning

confidence: 99%

Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

Akgün

Dang

Miguel

et al. 2020

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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We extend automatic instance generation methods to allow cross-paradigm comparisons. We demonstrate that it is possible to completely automate the search for benchmark instances that help to discriminate between solvers. Our system starts from a high level humanprovided problem specification, which is translated into a specification for valid instances. We use the automated algorithm configuration tool irace to search for instances, which are translated into inputs for both MIP and CP solvers by means of the Conjure, Savile Row, and MiniZinc tools. These instances are then solved by CPLEX and Chuffed, respectively. We constrain our search for instances by requiring them to exhibit a significant advantage for MIP over CP, or vice versa. Experimental results on four optimisation problem classes demonstrate the effectiveness of our method in identifying instances that highlight differences in performance of the two solvers.

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Section: Discussionmentioning

confidence: 99%

Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

Akgün

Dang

Miguel

et al. 2020

Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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“…The degree of the amount varies i.e. wide range of diverse solutions [20] in the population, and the way one-of-a-type they're (distance between possibility answers).…”

Section: Diversitymentioning

confidence: 99%

AP: Hybrid Task Scheduling Algorithm for Cloud

Dewangan¹,

Jain²,

Choudhury³

2020

RIA

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Resource optimization is cost effective process in cloud. The efficiency of load balancing completely depends on how the infrastructure is utilizing. As per the current study, the resource optimization techniques are very costly and taking more convergence time to execute the task and load distribution among different virtual machines (VM). The objective of this paper is to develop a hybrid optimization algorithm to find the best virtual machine based on their fitness values and schedule different task to the fittest VM so that each task should get complete on time, and system can utilize the VM as well. The proposed algorithm is hybrid version of genetic (GA), ant-colony (Aco), and particle-swarm (Pso) algorithms, which is implemented and tested in amazon web service and compared with existing algorithms based on VM utilization, completion time, and cost. The proposed hybrid system genetic-aco-pso based algorithm (GAP) perform utmost while comparing with the existing systems.

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“…This article is extended from its conference version which was published in PPSN 2016 (Gao et al, 2016). The approach proposed in the conference paper has been applied in artistic image variants analysis (Alexander et al, 2017) and examined with different diversity measurement such as discrepancy (Neumann et al, 2018) and popular indicators from the area of evolutionary multiobjective optimisation (Neumann et al, 2019). Our further research focuses on a weighted version of feature-based population diversity measurement and involving problem hardness as an extra feature value, which will be detailed in Sections 5.2 and 7.…”

Section: Introductionmentioning

confidence: 99%

Feature-Based Diversity Optimization for Problem Instance Classification

Gao

Nallaperuma

Neumann

2021

Evolutionary Computation

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Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

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Evolutionary diversity optimization using multi-objective indicators

Cited by 44 publications

References 26 publications

Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP

AP: Hybrid Task Scheduling Algorithm for Cloud

Feature-Based Diversity Optimization for Problem Instance Classification

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