Results 1 to 10 of about 321,764 (251)
Evolving Evolutionary Algorithms using Linear Genetic Programming [PDF]
Evolutionary Computation, MIT Press, Vol. 13, Issue 3, pp.
387-410, 2005, 2021 A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment ...
Mihai Oltean
arxiv +3 more sources
Evolving Evolutionary Algorithms using Multi Expression Programming [PDF]
European Conference on Artificial Life, LNCS 2801, pp. 651-658,
2003, 2021 Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem.
Mihai Oltean, Crina Groşan
arxiv +3 more sources
A discipline of evolutionary programming
Theoretical Computer Science, 2000 Genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions. We propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on small size populations. Based on rapidly mixing Markov chains, the approach pertains to most
Paul Vitányi
openaire +6 more sources
Evolutionary Algorithms and Dynamic Programming [PDF]
Theoretical Computer Science, Vol. 412, Issue 43, 2011,
P.6020-6035, 2013 Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion.
Doerr, B. +4 more
arxiv +6 more sources
Evolutionary Improvement of Programs [PDF]
IEEE Transactions on Evolutionary Computation, 2011 Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption.
David White +2 more
openaire +4 more sources
The role of concurrency in an evolutionary view of programming abstractions [PDF]
arXiv, 2015 In this paper we examine how concurrency has been embodied in mainstream programming languages. In particular, we rely on the evolutionary talking borrowed from biology to discuss major historical landmarks and crucial concepts that shaped the development of programming languages.
Crafa, Silvia
arxiv +3 more sources
A spatial agent-based model for hydraulic fracturing water distribution
Frontiers in Environmental Science, 2022 Agent-based modeling (ABM) has been employed to understand and capture the complexity of the coupled human-nature processes in water resource systems. One of the challenges is to model human decisions in the coupled human and natural systems.
Tong Lin +7 more
doaj +1 more source
Quadrupedal Robots’ Gaits Identification via Contact Forces Optimization
Applied Sciences, 2021 The purpose of the present paper is the identification of optimal trajectories of quadruped robots through genetic algorithms. The method is based on the identification of the optimal time history of forces and torques exchanged between the ground and ...
Gianluca Pepe +3 more
doaj +1 more source
Energies, 2021 Economic Dispatch (ED) problems have been solved using single-objective optimization for so long, as Grid System Operators (GSOs) previously only focused on minimizing the total production cost.
Mohd Helmi Mansor +2 more
doaj +1 more source
Algorithms, 2021 Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function.
Jalal Al-Afandi, András Horváth
doaj +1 more source