Genetic Programming Lifelong Multitasking Evolution: LLGP-Tasking

We present a Lifelong Multi-Tasking learning algorithm based on Genetic Programming referred to as “LLGPTasking”. This paper extends previously published work ”GPTasking” kattanSSCI2020GPTASKING, evolving a population of GP trees using a multifaceted strategy. In GP-Tasking, each individual is trained with multiple fitness functions (where each function represents one task and has different training/testing sets). Empirical evidence demonstrated that the quality of evolved solutions is comparable to standard GP achieving significantly faster computational time while maintaining smaller evolved population sizes. In this work, we improved GP-Tasking and introduced a new crossover mechanism to transfer useful knowledge across different tasks. Further, we introduced new population initialisation approach to accumulate knowledge across different domains. The new LLGP-Tasking can solve multiple problems simultaneously and receive sequentially new batches of problems, Experimental results of the new LLGPTasking demonstrate superiority of evolved solutions over standard GP and it maintained same search speed produced by its predecessor (i.e., GP-Tasking).