Evolutionary algorithms
Preference-guided evolutionary algorithms for optimization with many objectives
... Multi-objective Evolutionary Algorithms, mainly the Pareto-based ones (like NSGA-II and SPEA2), were very successful when solving a lot of real world problems (see [22] for some examples), such that in some ...
143
Transgenic, an operator for Evolutionary Algorithms
... studied evolutionary systems with the idea that evolution could be used as an optimization tool for engineer- ing ...these evolutionary-computation researchers, the mechanisms of evolution seem well suited ...
7
3D flight route optimization for air-taxis in urban areas with evolutionary algorithms
... The results proved, that the flight routes represented by geographical point and line objects can be optimized with Evolutionary Algorithms for multiple criteria. Furthermore, the results illustrated that ...
50
Classification Of Complex UCI Datasets Using Machine Learning And Evolutionary Algorithms
... 2.7 Genetic Programming: It is an evolutionary learning technique that offers a great potential for classification. The application of GP to classification offers some interesting advantages such as flexibility ...
10
How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?
... three algorithms searched the same 13 SAC-SMA parameters (3 parameters are commonly fixed a priori) and parameter ranges as were specified by Vrugt et ...The algorithms were tested on their ability to ...
19
Multiobjective optimization of classifiers by means of 3-D convex Hull based evolutionary algorithms
... There are two key steps of any EMOAs, one is how to rank the population and the other one is selecting solutions that survive to the next generation. The ranking approach of NSGA-II, probably the most popular EMOA used ...
33
Affine image registration using genetic algorithms and evolutionary strategies
... Evolutionary algorithms (EA) have several branches including genetic algorithms, evo- lutionary strategies, evolutionary programing, and many ...the evolutionary operators including ...
117
Evolutionary model tree induction
... The greatest challenge of this work was the continuously attempt of reducing E-Motion’s execu- tion time. As most evolutionary algorithms, E-Motion is very time-consuming. Due to the fact it is a ...
84
Procedural Optimization Models for Multiobjective Flexible JSSP
... are: evolutionary algorithms in general and genet- ic algorithms in particular, agent-based mod- els (negotiation techniques, Ant Colony Op- timization, Particle Swarm Optimization, Wasp Behavior ...
12
Abstract — The physical topology design (PTD) of optical
... A heuristics can be defined as an approach that uses specific information of the problem to obtain a reasonable solution. Most of the works related to heuristics for PTD uses variations of branch exchange (BE) [7] or cut ...
14
Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.
... There is also an assumption that, given our ability to generate knowledge of genetic sequences (and markers) at massively increasing rates (e.g. [36,37]), knowledge of parental genotypes should somehow better inform the ...
14
Maximally Distant Codes Allocation Using Chemical Reaction Optimization with Enhanced Exploration
... methods. Evolutionary paradigms were applied with varying degrees of success; for example, the work in [1] described some initial experiments of Genetic Algorithms (GAs) to discover maximal distance codes, ...
9
An Optimal Scheduling Algorithm for Real Time Applications in Grid System
... meets its deadline. In dynamic scheduling, however, the system may be unable to meet the deadlines of all tasks because it lacks knowledge about future task arrivals. As the history of the field suggest there are many ...
6
IMPACT OF ALIFE SIMULATION OF DARWINIAN AND LAMARCKIAN EVOLUTIONARY THEORIES
... using evolutionary algorithms originate from the work of Barricelli in the 1960s, continued by Fraser, who published a series of papers on simulation of artificial selection (Fraser, ...Genetic ...
43
A New Strategy to Optimize the Load Migration Process in Cloud Environment
... answer). Evolutionary algorithms such as this one begin with an initial solution which is the starting point for moving toward optimal solution produced ...
7
Evolutionary Robotics
... three evolutionary algorithms yields an important advantage: enabling the robot to recover from unanticipated situations such as physical damage to one of its legs (Bongard et ...
14
Breeding terrains with genetic terrain programming: the evolution of terrain generators
... 2.2. Evolutionary Terrain Generation Techniques. Evolution- ary algorithms (EAs) are a kind of bioinspired algorithms that apply Darwin’s theory [12] of natural evolution of the species, where living ...
13
Hybrid Genetic Algorithms: A Review
... combined evolutionary algorithms with random linkage and borrowed the concept of short memory from tabu search [129] to avoid performing unnecessary local search on non-promising regions of the search ...
14
Alternative Method for Solving Traveling Salesman Problem by Evolutionary Algorithm
... on Evolutionary Algorithms that are originally focused on solving non-linear programming problems that contain continuous ...of Evolutionary Algorithm requires some special approaches to guarantee ...
6
Non-dominated Sorting Genetic Algorithms for Heterogeneous Embedded System Design
... Pareto evolutionary algorithms to this problem proposed in does not converge to true Pareto-optimal solutions, because that method uses the fitness assignment procedure, which is very sensitive to concave ...
4