Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al

We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared with two state-of-the-art multiobjective optimizers, NSGA-II and SPEA2. The results obtained indicate that, according to the benchmark and parameter settings used, AbYSS outperforms the other two algorithms as regards the diversity of the solutions, and it obtains very competitive results according to the convergence to the true Pareto fronts and the hypervolume metric.

Since the 1960s researcher have developed various storage assignment strategies for different warehouse scenarios. Much of this research has been devoted to re-warehousing, which involves extensive re-arrangements akin to filling a warehouse from scratch. However, due to seasonal fluctuations in demand or product mix, operations managers need to periodically review item placement in practise and re-organize the warehouse to keep it operating efficiently. In this paper we demonstrate for a sample warehouse how applying a small number of cleanup tasks (=healing) every day will lead to a good total warehouse assignment over the course of many months and show that the results are competitive to re-warehousing.

Formal fitness landscape analysis enables us to study basins of attraction with great detail. This seemingly simple concept shows more variability and influence on heuristic algorithms than might be expected. We have taken a new perspective and a closer look at the properties of basins of attraction using two-dimensional visualizations to arrive at a conceptually simple but heuristically challenging test function that exhibits “misleading” basins of attraction.