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The process consisted in an evolutionary algorithmic for optimizing the shape of the building's roof in order to maximize the use of solar energy.
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Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems.
This paper focuses on the algorithmic approach for this class of problems, and develops an evolutionary algorithm based on a dynamic weighted aggregation method.
The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms.
Evolutionary algorithms (EAs) are popular in solving a diversity of problems, but current algorithm design approaches typically require formulating an algorithmic structure for each individual problem.
These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.
Evolutionary algorithm.
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems.
Rapidly increasing computer power has allowed the use of more sophisticated algorithmic designs and evolutionary methods.
Instead, the authors strived to overview the problem of defining algorithmic construction of the evolutionary scenario as a central concept in many areas of evolutionary research.
We denote this class of hybrid algorithmic techniques as the evolutionary computation versus reinforcement learning (ECRL) paradigm.
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