What are hyper-heuristics?

Hyper-heuristics are high-level problem-solving approaches that aim to automate the design of heuristics or heuristic search algorithms. In other words, a hyper-heuristic is a method that seeks to find or generate other heuristics rather than directly solving a specific problem. It operates at a higher level of abstraction and deals with the exploration and manipulation of a set of heuristics or heuristic components rather than focusing on the problem instances themselves.

Traditionally, designing heuristics involves human expertise and intuition, which is quite time-consuming and might not guarantee the best performance. Hyper-heuristics, on the other hand, aim to address these limitations by automating the process of heuristic design.

Key characteristics of hyper-heuristics include:

• Generality: Hyper-heuristics are applicable to a wide range of problems, making them more versatile than specialized heuristics tailored to specific problem domains.

• Adaptability: Hyper-heuristics can adapt to different problem instances and adjust the behavior of the underlying heuristics based on problem-specific features.

• Diversity: Hyper-heuristics typically maintain a diverse set of heuristics or heuristic components, which helps explore different areas of the search space and avoid getting stuck in local optima.

• Learning: Some hyper-heuristics use machine learning techniques to improve the performance of the heuristic selection process based on past experiences or problem instances.

Hyper-heuristics have been successfully applied to various optimization problems, such as scheduling, routing, bin packing, and more. They offer a promising approach for developing more automated and adaptable algorithms that can deliver solutions for a wide range of problems without the need for extensive manual work.

Types of hyper-heuristics

Hyper-heuristics are categorized into different types based on their approaches and processes. The following are the main types of hyper-heuristics:

Selection hyper-heuristics:

• Low-level heuristic selection: This involves selecting low-level heuristics from a set of available heuristics to solve a problem.

• Heuristic selection mechanisms: Algorithms that use various strategies, such as reinforcement learning, evolutionary algorithms, machine learning techniques, and so on, to select the most appropriate heuristic based on problem characteristics or historical performance.

Generation hyper-heuristics:

• Heuristic generation: Creating new heuristics or combining existing ones to form new heuristics that can solve a given problem.

• Search and optimization techniques: Employing search algorithms (like genetic algorithms, simulated annealing, or particle swarm optimization) to explore the space of possible heuristics and identify effective combinations or new heuristics.

Processes involved in hyper-heuristics

The development of hyper-heuristics involves several processes, as seen in the diagram below, that contribute to creating effective algorithms capable of dynamically selecting, generating, or adapting heuristics to solve computational problems. Here are the key processes involved in making hyper-heuristics:

• Initialization: Initialization involves defining the initial set of heuristics or generating an initial population of heuristics to start the hyper-heuristic process.

• Evaluation: This involves evaluating the performance of heuristics based on predefined criteria or fitness measures to determine their effectiveness in solving the problem.

• Selection/Generation: Here, we choose heuristics from the available set based on certain criteria, or we create and modify heuristics by combining existing ones.

• Application: This involved applying the chosen heuristics.

• Adaptation and improvement: Here, we analyze the performance of the heuristics, and based on that, we adapt our process of selecting and generating heuristics.

• Termination: This involves determining the stopping criteria, whether based on a certain number of iterations, reaching a performance threshold, or other defined conditions.

Overall, hyper-heuristics represent a powerful and flexible approach to optimization, leveraging the strengths of multiple heuristics and adaptive learning to tackle complex problems more effectively.