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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Search Trajectory Networks: Analyzing Stochastic Optimization Algorithms Graphically

Search Trajectory Networks: Analyzing Stochastic Optimization Algorithms Graphically

Ochoa, Gabriela
Blum, Christian
Chacón Sartori, Camilo

Paperback

Series: Springerbriefs in Intelligent Systems

General ComputersProbability & Statistics

PREORDER - Expected ship date March 6, 2027

ISBN10: 3032428688
ISBN13: 9783032428684
Publisher: Springer
Published: Mar 6 2027
Language: English

Visual representations of complex concepts enhance our ability to understand digital information more effectively, particularly in fields like Computer Science and Artificial Intelligence. Despite this, the research community in optimization has been relatively unproductive in developing visual tools, even though there is a growing need for such tools to aid in comparing optimization algorithms. Traditionally, the comparison of optimization algorithms has relied on collecting numerical data from optimization runs and presenting them through tables and conventional data visualizations (e.g., line plots, bar plots, scatter plots). This has typically been supplemented by statistical analyses of the data. However, in recent years, more researchers have recognized that to gain a deeper understanding of the behavior of optimization algorithms, such as metaheuristics, additional graphical tools are necessary. Furthermore, these tools must be user-friendly to facilitate their use.

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