Long Term Memory Assistance for Evolutionary Algorithms

Abstract

Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.

Bat4CEP: a bat algorithm for mining of complex event processing rules

“…During the evolutionary process, duplicate rules are especially likely to be generated when the rule trees are still small. (Long Term Memory Assistant for Evolutionary Algorithms) introduced in [52] is an approach to discover re-visited individuals and reuse already calculated fitness values. It can significantly improve the runtime behavior and should be integrated in next versions of Bat4CEP.…”

A JSSP solution for production planning optimization combining industrial engineering and evolutionary algorithms

“…The algorithm can be improved further using Long Term Memory Assistance (LTMA) [24], where duplicate solutions are identified. As such, time-consuming fitness evaluation is spared.…”

Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

“…However, to set the upper limit of the H(v) evaluations to be equal to the number of evaluations in the exhaustive search variant (Algorithm 5), we have used a simple memoization technique [27]. Memoization is often used in dynamic programming and recently also to improve the performance of metaheuristic algorithms [28]; however, it should be noted that it leads to an increase in memory usage. However, as pointed out in [28], it is hard to imagine this as an issue, given that the advances in hardware technology lead to massive capacities of inexpensive memory.…”

“…Memoization is often used in dynamic programming and recently also to improve the performance of metaheuristic algorithms [28]; however, it should be noted that it leads to an increase in memory usage. However, as pointed out in [28], it is hard to imagine this as an issue, given that the advances in hardware technology lead to massive capacities of inexpensive memory. The memoized version of the coordinate-descent algorithm for optimum histogram binning v is given in Algorithm 7.…”