Implementation issues for an interactive evolutionary computation system

Shackelford, M.

doi:10.1145/1274000.1274100

Cited by 14 publications

(11 citation statements)

References 9 publications

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“…As presented in the Introduction, when the DM is not employed in the resolution process, he/she may feel excluded in the analysis, which may cause resistance and lack of confidence in the final result (Miettinen 1999,Shackelford 2007). …”

Section: How the Preferences Are Incorporated And Influence The Searcmentioning

confidence: 99%

“…Moreover, when the DM is not employed in the resolution process, he/she may feel excluded from the analysis, and present some resistance or put little confidence in the final result (Miettinen 1999,Shackelford 2007). In addition, there are several intrinsic aspects of requirements engineering which comprehension is inherently subjective, demanding a more effective user collaboration (Harman 2007b).…”

Section: Introductionmentioning

confidence: 99%

“…Population-based algorithms seem to be ideal for interactive optimization since a user can directly evaluate fitness values of potential solutions (Takagi 2001). This optimization strategy is often necessary when the algorithm that defines the fitness function is either too complex to specify or, in many cases, impossible to quantify and code (Shackelford 2007).…”

Section: Introductionmentioning

confidence: 99%

“…This exhaustion occurs due to repeated requests for human judgements, which ends up distracting the focus of the user over the algorithm execution, then jeopardizing the search trajectory . Dealing with this intrinsic complication can be considered a mandatory feature for any interactive optimization approach (Shackelford 2007).…”

Section: Introductionmentioning

confidence: 99%

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An Architecture based on interactive optimization and machine learning applied to the next release problem

et al. 2016

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The next release problem (NRP) consists of selecting which requirements will be implemented in the next release of a software system. For many search based software engineering approaches to the NRP, there is still a lack of capability to efficiently incorporate human experience and preferences in the search process. Therefore, this paper proposes an architecture to deal with this issue, where the decision maker (DM) and his/her tacit assessments are taken into account during the solutions evaluations alongside the interactive genetic algorithm. Furthermore, a learning model is employed to avoid an overwhelming number of interactions. An empirical study involving software engineer practitioners, different instances, and different machine learning techniques was performed to assess the feasibility of the architecture to incorporate human knowledge in the overall optimization process. Obtained results indicate the architecture can assist the DM in selecting a set of requirements that properly incor-B Allysson Allex Araújo allysson.araujo@uece.br 123 Autom Softw Eng porate his/her expertise, while optimizing other explicit measurable aspects equally important to the next release planning. On a scale of 0 (very ineffective) to 5 (very effective), all participants found the experience of interactively selecting the requirements using the approach as a 4 (effective).

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Section: How the Preferences Are Incorporated And Influence The Searcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Architecture based on interactive optimization and machine learning applied to the next release problem

et al. 2016

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“…However, only one attempt has been made to apply this technique, which can incorporate human expert knowledge directly into fit-ness computation, in project management (Shackelford and Corne 2001). Since a software project management task is inherently human-centric it would be natural to explore the use of interactive evolution as a technique for ensuring that the project manager's expertise is accounted for in software project management (Shackelford 2007). The difficulty, as with all interactive evolution, lies in finding a way in which the manager can influence the computation of fitness without overburdening him or her with request for 'fitness assessment'.…”

Section: Interactive Optimisationmentioning

confidence: 99%

Search-Based Software Project Management

Ferrucci

Harman

Sarro

2014

Software Project Management in a Changing World

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Abstract. Project management presents the manager with a complex set of related optimisation problems. Decisions made can more profoundly affect the outcome of a project than any other activity. In the chapter, we provide an overview of Search-Based Software Project Management, in which Search-Based Software Engineering (SBSE) is applied to problems in software project management. We show how SBSE has been used to attack the problems of staffing, scheduling, risk, and effort estimation. SBSE can help to solve the optimisation problems the manager faces, but it can also yield insight. SBSE therefore provides both decision making and decision support. We provide a comprehensive survey of Search-Based Software Project Management, and give directions for the development of this subfield of SBSE. IntroductionSoftware Project Management includes several activities critical for the success of a project (e.g., cost estimation, project planning, quality management). These activities often involve finding a suitable balance between competing and potentially conflicting goals. For example, planning a project schedule requires to minimise the project duration and the project cost, and to maximise the product quality. Many of these problems are essentially optimization questions characterised by competing goals/constraints and with a bewilderingly large set of possible choices. So finding good solutions can be hard.Search-Based Software Engineering seeks to reformulate software engineering problems as search-based optimisation problems and applies a variety of metaheuristics based on local and global search to solve them (such as Hill Climbing, Tabu Search, and Genetic Algorithms). These meta-heuristics search for a suitable solution in a typically large input space guided by a fitness function that expresses the goals and leads the exploration into potentially promising areas of the search space.Though the term Search-Based Software Engineering (SBSE) was coined by Harman and Jones in 2001 to cover the application of computational search and optimisation across the wide spectrum of software engineering activities (Harman and Jones 2001), there were already pockets of activity on several specific software engineering problems prior to the introduction of the term SBSE. One such topic was Search-Based Software Project Management, the topic of this chapter. In particular, there was work on search-based project scheduling and staffing by

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Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

Huber

Geiger

Sevaux

2014

Multi Criteria Decision Anal

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International audienceIn the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search-and-decision-making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem

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Implementation issues for an interactive evolutionary computation system

Cited by 14 publications

References 9 publications

An Architecture based on interactive optimization and machine learning applied to the next release problem

An Architecture based on interactive optimization and machine learning applied to the next release problem

Search-Based Software Project Management

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

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