looking back and looking forward

Mcdermott, J., Kronberger, G., Orzechowski, P., Vanneschi, L., Manzoni, L., Kalkreuth, R., & Castelli, M. (2022). Genetic programming benchmarks: looking back and looking forward. ACM SIGEVOlution, 15(3), 1-19. https://doi.org/10.1145/3578482.3578483The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.authorsversionpublishe

An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialised for a single domain. Metaheuristics and hyper-heuristics are typically used as high-level cross-domain search methods, utilising problem-specific low-level heuristics for each problem domain to modify a solution. Such methods have a number of parameters to control their behaviour, whose initial settings can influence their search behaviour significantly. Previous methods in the literature either fix these parameters based on previous experience, or set them specifically for particular problem instances. There is a lack of extensive research investigating the tuning of these parameters systematically. In this paper, F-Race is deployed as an automated cross-domain parameter tuning approach. The parameters of a steady-state memetic algorithm and the low-level heuristics used by this algorithm are tuned across nine single-objective problem domains, using different training strategies and budgets to investigate whether F-Race is capable of effectively tuning parameters for cross-domain search. The empirical results show that the proposed methods manage to find good parameter settings, outperforming many methods from the literature, with different configurations identified as the best depending upon the training approach used

Fitness landscape analysis of a class of np-hard problems

A number of fitness landscape properties of randomly generated instances of a class of NP-hard combinatorial optimisation problems are empirically studied in this research. We believe that the studied properties give insight into the structure of the problem landscape and can be representative of the problem difficulty, in particular with respect to local search algorithms. The properties include: types of search position, number of local and global optima and plateaux, quality of optima and plateaux, basin size and its correlation with fitness, time to local optima, cost of finding the global solution, and the quality of optima obtained with a fixed budget search. Our work focuses on studying how these properties vary with different values of problem parameters. We also compare these properties across different landscapes that were induced by different neighbourhood operators or different penalty functions of the following problems: the number partitioning problem, the binary knapsack problem, and the quadratic binary knapsack problem. Unlike existing studies of these problems, we study instances generated at random from various distributions. We found a general trend where in all the three problems, some of their landscape features were found to vary between the different distributions. We captured this variation by a single, easy to calculate, parameter and we showed that it has a potentially useful application in guiding the choice of the neighbourhood operator of local search heuristics