{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T12:31:59Z","timestamp":1759667519637},"reference-count":26,"publisher":"MIT Press - Journals","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2014,9]]},"abstract":"<jats:p> Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors\u2019 knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances. <\/jats:p>","DOI":"10.1162\/evco_a_00112","type":"journal-article","created":{"date-parts":[[2013,10,8]],"date-time":"2013-10-08T18:16:24Z","timestamp":1381256184000},"page":"361-403","source":"Crossref","is-referenced-by-count":9,"title":["On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks"],"prefix":"10.1162","volume":"22","author":[{"given":"F. V. C.","family":"Martins","sequence":"first","affiliation":[{"name":"Department of Computing and Information Systems, Universidade Federal de Ouro Preto, Jo\u00e3o Monlevade, 359301-026, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. G.","family":"Carrano","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. F.","family":"Wanner","sequence":"additional","affiliation":[{"name":"Department of Computing, Centro Federal de Educa\u00e7\u00e3o Tecnol\u00f3gica de Minas Gerais, Belo Horizonte, 30480-000, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. H. C.","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. 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