Recent developments of evolutionary algorithms (EAs) for discrete optimization problems
are often characterized by the hybridization of EAs with local search methods, in particular …

To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search
for optimal solutions from zero initial information without taking advantage of the historical …

Abstract The Lin-Kernighan-Helsgaun (LKH) algorithm is one of the most successful search
algorithms for the Traveling Salesman Problem (TSP). The core of LKH is a variable depth …

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple
traveling salesman problem, which introduces colors to distinguish the accessibility of its …

A method of heuristically solving large traveling salesman problems is suggested, where a
dramatic computational speedup is guaranteed. A specific genetic algorithm is the solver …

We study both genotypic and phenotypic convergence in GP floating point continuous
domain symbolic regression over thousands of generations. Subtree fitness variation across …

A genetic algorithm using Edge Assemble Crossover (EAX) is one of the best heuristic
solvers for large instances of the Traveling Salesman Problem. We propose using Partition …

Recombination is a powerful way of generating new solutions in Evolutionary Algorithms.
There are many ways to implement recombination. Traditional recombination operators do …

Local optima networks (LONs) model the global distribution and connectivity pattern of local
optima under given search operators. Recent research has looked at how recombination …

In many applications of evolutionary algorithms the computational cost of applying operators
and storing populations is comparable to the cost of fitness evaluation. Furthermore, by …