Genetic algorithms (GA) are among the widely used in various areas of computer science, including optimization problems. Genetic algorithms (GA) are based on the biological process of natural selection. Many simulations have demonstrated the efficiency of GAs on different optimization problems, among them, bin-packing, qaudratic assignment problem, graph partitioning, job-shop scheduling problem, set covering problem, traveling salesman problem, vehicle routing. The quadratic assignment problem (QAP) belong to the class of NP-hard combinatorial optimization problems. One of the main operators in GA is a crossover (i.e. solution recombination). This operator plays a very important role by constructing competitive genetic algorithms (GAs). In this work, we investigate several crossover operators for the QAP, among them, ULX (uniform like crossover), SPX (swap path crossover), OPX (one point crossover), COHX (cohesive crossover), MPX (multiple parent crossover) and others. Comparison of these crossover operators was performed. The results show high efficiency of the cohesive crossover.

No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.