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Heuristics : intelligent search strategies for computer problem solving

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Metaheuristics: Intelligent Problem Solving

• First Online: 01 January 2009

Cite this chapter

• Marco Caserta 4 &
• Stefan Voß 4  

Part of the book series: Annals of Information Systems ((AOIS,volume 10))

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Metaheuristics support managers in decision making with robust tools providing high quality solutions to important problems in business, engineering, economics and science in reasonable time horizons. While finding exact solutions in these applications still poses a real challenge despite the impact of recent advances in computer technology and the great interactions between computer science, management science/operations research and mathematics, (meta-) heuristics still seem to be the methods of choice in many (not to say most) applications. In this chapter we give some insight into the state of the art of metaheuristics. It focuses on the significant progress regarding the methods themselves as well as the advances regarding their interplay and hybridization with exact methods.

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Caserta, M., Voß, S. (2009). Metaheuristics: Intelligent Problem Solving. In: Maniezzo, V., Stützle, T., Voß, S. (eds) Matheuristics. Annals of Information Systems, vol 10. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1306-7_1

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Computer Science > Computation and Language

Title: $t^2$ of thoughts: temperature tree elicits reasoning in large language models.

Abstract: Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, especially in complex decision-making scenarios, but their static problem-solving strategies often limit their adaptability to dynamic environments. We explore the enhancement of reasoning capabilities in LLMs through Temperature Tree ($T^2$) prompting via Particle Swarm Optimization, termed as $T^2$ of Thoughts ($T^2oT$). The primary focus is on enhancing decision-making processes by dynamically adjusting search parameters, especially temperature, to improve accuracy without increasing computational demands. We empirically validate that our hybrid $T^2oT$ approach yields enhancements in, single-solution accuracy, multi-solution generation and text generation quality. Our findings suggest that while dynamic search depth adjustments based on temperature can yield mixed results, a fixed search depth, when coupled with adaptive capabilities of $T^2oT$, provides a more reliable and versatile problem-solving strategy. This work highlights the potential for future explorations in optimizing algorithmic interactions with foundational language models, particularly illustrated by our development for the Game of 24 and Creative Writing tasks.

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