Evolutionary Algorithms and Dynamic Programming
This provides a method for solving combinatorial optimization problems more efficiently, but it is incremental as it builds on existing evolutionary and dynamic programming approaches.
The paper tackles the problem of designing evolutionary algorithms that incorporate dynamic programming techniques, showing that for a class of DP-benevolent problems, there exists a fully polynomial-time randomized approximation scheme based on an evolutionary algorithm.
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. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how to develop evolutionary algorithms that have the additional ability of carrying out dynamic programming steps. Finally, we show that for a wide class of the so-called DP-benevolent problems (which are known to admit FPTAS) there exists a fully polynomial-time randomized approximation scheme based on an evolutionary algorithm.