Applications of evolutionary strategies
Systems Analysis Modelling Simulation
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Parallel Genetic Algorithms Population Genetics and Combinatorial Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
The Density of States - A Measure of the Difficulty of Optimisation Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Network Optimization Using Evolutionary Strategies
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The science of breeding and its application to the breeder genetic algorithm (bga)
Evolutionary Computation
Teaching students to use genetic algorithms to solve optimization problems
Proceedings of the seventh annual consortium for computing in small colleges central plains conference on The journal of computing in small colleges
Exact Solutions for Evolutionary Strategies on Harmonic Landscapes
Evolutionary Computation
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A road network usually has to fulfill two requirements: (i) it should as far as possible provide direct connections between nodes to avoid large detours; and (ii) the costs for road construction and maintenance, which are assumed proportional to the total length of the roads, should be low. The optimal solution is a compromise between these contradictory demands, which in our model can be weighted by a parameter. The road optimization problem belongs to the class of frustrated optimization problems. In this paper, a special class of evolutionary strategies, such as the Boltzmann and Darwin and mixed strategies, are applied to find differently optimized solutions (graphs of varying density) for the road network, depending on the degree of frustration. We show that the optimization process occurs on two different time scales. In the asymptotic limit, a fixed relation between the mean connection distance (detour) and the total length (costs) of the network exists that defines a range of possible compromises. Furthermore, we investigate the density of states, which describes the number of solutions with a certain fitness value in the stationary regime. We find that the network problem belongs to a class of optimization problems in which more effort in optimization certainly yields better solutions. An analytical approximation for the relation between effort and improvement is derived.