Effective and Efficient Caching in Genetic Algorithms

Abstract

Hard discrete optimization problems using randomized methods such as genetic algorithms require large numbers of samples from the solution space. Each candidate sample/solution must be evaluated using the target fitness/energy function being optimized. Such fitness computations are a bottleneck in sampling methods such as genetic algorithms. We observe that the caching of partial results from these fitness computations can reduce this bottleneck. We provide a rigorous analysis of the run-times of GAs with and without caching. By representing fitness functions as classic Divide and Conquer algorithms, we provide a formal model to predict the efficiency of caching GAs vs. non-caching GAs. Finally, we explore the domain of protein folding with GAs and demonstrate that caching can significantly reduce expected run-times through both theoretical and extensive empirical analyses.

Reducing the computational load of energy evaluations for protein folding

“…In [10], the approach of caching partial results was introduced. Divide and conquer is a classic algorithm design paradigm.…”

“…Divide and conquer is a classic algorithm design paradigm. Below is the skeletal structure of a divide and conquer algorithm [10]: Divide I into smaller instances I 1 , I 2 , · · · I k with problem sizes n 1 , n 2 , · · · n k , resp.…”

“…By taking into account caching, we modify the divide and conquer scheme for the fitness function evaluation [10].…”

“…This is a modification of the one for square lattices in [10]. However, since for this paper, implementation utilizes 2-point crossover and the amount of potential neighbors increases to 6, new insights were gained.…”

“…Recently, Santos and Santos [10] proposed a method for caching and re-using partial fitness evaluation results. They applied their approach to 2D and 3D square lattice HP models and formally predicted a 50% savings in fitness evaluations using their caching approach.…”