Helper-objective optimization strategies for the Job-Shop Scheduling Problem

Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction

“…In a recent review , a distinction is made between multi-objectivization proposals where supplementary objectives are problem-dependent and are computed based solely on information from the solution under consideration (Greiner et al, 2007;Jähne et al, 2009;Jensen, 2004;Lochtefeld & Ciarallo, 2011, and those where they act as diversity measures (Bui, Nguyen, Branke, & Abbass, 2008;Mouret, 2011;Segredo, Segura, & Leon, 2011;Sharma et al, 2014;Tran, Brockhoff, & Derbel, 2013;Wessing, Preuss, & Rudolph, 2013). Separate treatment is given also to those proposals where additional objectives are implemented to handle constraints (Churchill et al, 2013;Singh, Ray, & Sarker, 2013).…”

“…For a recent review on applications of multi-objectivization, the reader can be referred to Segura, Coello Coello, Miranda, and León (2013). Multi-objectivization has been largely studied in the context of well-known combinatorial problems such as the traveling salesman problem (Jähne, Li, & Branke, 2009;Jensen, 2004;Knowles et al, 2001;Lochtefeld & Ciarallo, 2014), the job-shop scheduling problem (Jensen, 2004;Lochtefeld & Ciarallo, 2011), the bin packing problem (Segredo, Segura, & León, 2013;, the vehicle routing problem (Watanabe & Sakakibara, 2007), and the shortest path and minimum spanning tree problems (Neumann & Wegener, 2008). Also, multi-objectivization has found interesting applications in the fields JID: EOR [m5G;20:40] of mobile communications (Segura, Segredo, González, & León, 2011;Segura, Segredo, & Leõn, 2013), computational mechanics (Greiner, Emperador, Winter, & Galván, 2007), power system planning (Trivedi, Sharma, & Srinivasan, 2012), structural topology optimization (Sharma, Deb, & Kishore, 2014), computer aided manufacturing (Churchill, Husbands, & Philippides, 2013), robotics (Mouret, 2011) and computer vision (Vite-Silva, Cruz-Cortés, Toscano-Pulido, & de la Fraga, 2007).…”

Adaptive selection of helper-objectives for test case generation

“…The strategy can be either random, or ad-hoc [9]. The random one is general, but it does not take advantage of problem characteristics.…”

Generation of tests for programming challenge tasks using multi-objective optimization

“…Performance time is a hard objective to be optimized, since it is noisy and takes only several different values, at least in the case of the considered problem. So we use the multi-objectivization approach [18,21] and the helperobjectives approach [16,19]. Both approaches are based on using some additional objectives to be optimized.…”