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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.

Multi-Objective Evolutionary Algorithms (MOEAs) have been applied successfully for solving real-world multi-objective problems. Their success can depend highly on the configuration of their control parameters. Different tuning methods have been proposed in order to solve this problem. Tuning can be performed on a set of problem instances in order to obtain robust control parameters. However, for real-world problems, the set of problem instances at our disposal usually are not very plentiful. This raises the question: What is a sufficient number of problems used in the tuning process to obtain robust enough parameters? To answer this question, a novel method called MOCRS-Tuning was applied on different sized problem sets for the real-world integration and test order problem. The configurations obtained by the tuning process were compared on all the used problem instances. The results show that tuning greatly improves the algorithms’ performance and that a bigger subset used for tuning does not guarantee better results. This indicates that it is possible to obtain robust control parameters with a small subset of problem instances, which also substantially reduces the time required for tuning.

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