Learning-Aided Evolution for Optimization

Abstract

Abstract-Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this paper proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on Evolutionary Computation competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.

Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization

“…For future work, the proposed algorithm will be further extended to solve more difficult and complex MOPs with different properties. As the proposed algorithm has some tunable parameters the number of potential points for approximating PF and the neighborhood size for knowledge transfer) that although have been investigated in the experimental part, we could use learning methods (e.g., learning-aided methods [71][72] and adaptive methods [73]- [76] ) in the future to configure the parameters more automatically and adaptively according to the target problem. Furthermore, as solving the MOP as a MTOP is a generic idea, further exploration of MTOP algorithms and knowledge transfer methods is worth studying to solve complex MOPs more efficiently.…”

Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey

“…Frontiers in Industrial Engineering frontiersin.org objectives has not been fully demonstrated (Zhan et al, 2023), indicating significant room for improvement in MOEAs driven by RL. This paper focuses on presenting recent achievements in the field of shop scheduling and presents potential challenges that shop scheduling might face in the future: First and foremost, research involving the utilization of EMOEAs to address shop scheduling problems remains in its nascent stage.…”

“…In the financial domain, it can be used to optimize investment portfolios to achieve better returns and risk control. Zhang Z. et al (2023) have proposed a cost-oriented hybrid model multi-person assembly line balancing approach to address the uncertain demand environment. They have also designed a MOEA based on RL to solve the problem.…”

Grid Classification-Based Surrogate-Assisted Particle Swarm Optimization for Expensive Multiobjective Optimization

“…Social learning PSO (SLPSO) [59] is a PSO [60][61] [62] variant that has shown good performance for large-scale single-objective optimization with high-dimension. As this paper also focuses on high-dimensional EMOPs, the SLPSO is adopted as the base optimizer.…”