Evolutionary based heuristic for bin packing problem

A grouping genetic algorithm with controlled gene transmission for the bin packing problem

“…Although the obtained performance is comparable to the HI_BP strategy from Alvim et al [8], the procedure C_BP presents a less robust behavior in terms of the number of times that it finds the optimal solutions. Stawowy [18] proposed the use of a non-specialized evolutionary strategy (ES) that includes the following: a modified permutation with a separators encoding scheme, a separators removal technique for problem size reduction and intelligent mutations. The results that were obtained by this procedure are comparable to other results from more complex algorithms, such as HGGA and HI_BP [7,8].…”

Resource-constrained scheduling for continuous repetitive projects with time-based production units

“…As opposed to genetic algorithms (GA), the ES algorithm is asexual since it requires only the mutation operator without the process of selection and crossover. With fewer tuning parameters to adjust, the ES algorithm is considered simple, and yet able to find competitive solutions of combinatorial problems in a reasonable amount of computing time [28,29]. Moreover, with no selection and crossover, the ES algorithm is easier to program and takes less execution time for the same generations.…”

A Novel Grouping Genetic Algorithm for the One-Dimensional Bin Packing Problem on GPU

“…The value k expresses a concentration on the almost full bins in comparison to less filled ones. Falkenauer used k = 2 but Stawowy reported that k = 4 gives slightly better results therefore, we prefer the second value [15].…”