Robust Outside-Range Optimization by Decoupling Search-Region Expansion and Search-Distribution Update in AREX/JGG

In real-coded genetic algorithms (RCGAs), search performance often deteriorates when the true global optimum lies outside the initial search range because crossover-based sampling remains concentrated around the initial population. We define this problem as outside-range optimization and introduce the outside-range ratio, representing the proportion of dimensions whose true optimum lies outside the initial search range. We extend Adaptive Real-coded Ensemble Crossover/Just Generation Gap (AREX/JGG) by explicitly decoupling search-region expansion and search-distribution update. Specifically, enhanced Chaotic Search (e-CS) expands the search region through extrapolation, AREX updates the search distribution, and Combinational Mutation (CM) generates candidates with heavy-tailed perturbations. Experiments on 10 benchmark functions with 20–80 dimensions showed lower expected running time (ERT) than AREX/JGG under many outside-range conditions examined. For 40-dimensional Sphere, Cigar, and k-tablet, ERT was lower than HIRCGA across all tested outside-range ratios and distances. Furthermore, for parameter estimation of an ordinary differential equation (ODE) model of Escherichia coli glycolysis, the proposed method achieved a higher success rate than AREX/JGG in three of four initial-range settings. These results suggest that the proposed method can improve search efficiency under many tested outside-range conditions and enhance the robustness of search success to initial-range settings.