Search Space

A Search Space is a conceptual space that encompasses all possible solutions or states for a given problem or optimization task.

• Context:
  • It can (typically) be in input to a Search Task.
  • It can (typically) represent the set of all possible solutions for a given problem.
  • It can (often) be defined by parameters and constraints that limit the possible solutions.
  • It can range from being a Discrete Search Space to being a Continuous Search Space.
  • It can be explored using various Search Algorithms such as Genetic Algorithms, Simulated Annealing, and A* Algorithm.
  • It can be affected by the presence of local optima, which can complicate the search process.
  • ...
• Example(s):
  • Algorithmic Search Spaces, such as:
    • a Sorting Algorithm Search Space ...
    • an Search Algorithm Space ...
    • a Machine Learning Algorithm Search Space exploring different ML model architectures.
    • a Language Model Algorithm Search Space exploring different LM model architectures.
    • a NP Algorithm Space ...
    • ...
  • Computing Search Spaces, such as:
    • a Discrete Search Space that includes all possible configurations of a combinatorial optimization problem.
    • a Continuous Search Space that represents all possible values within a given range for an optimization problem.
    • a Genetic Algorithm Search Space where potential genetic combinations are evaluated.
    • a Simulated Annealing Search Space exploring different states to minimize energy in a physical system.
    • a Hyperparameter Search Space where various machine learning hyperparameters are tuned.
  • Pathfinding Search Spaces, such as:
    • a Pathfinding Search Space represented as a graph with nodes and edges.
  • Machine Learning Search Spaces, such as:
    • a Feature Selection Search Space in a machine learning model where different subsets of features are evaluated.
    • a Neural Architecture Search Space where different neural network structures are explored.
  • Engineering Search Spaces, such as:
    • a Parameter Tuning Search Space in control systems to optimize performance.
    • a Design Search Space in engineering where different design parameters are considered.
  • Biological Search Spaces, such as:
    • a Protein Search Space where different protein structures and configurations are explored for drug design.
    • a Genome Search Space where various genetic sequences are analyzed for mutations.
  • Decision Making Search Spaces, such as:
    • a Life Choices Search Space where different career and personal decisions are considered.
    • a Business Strategy Search Space exploring different approaches for company growth.
    • a Policy Development Search Space where various policy options are evaluated for societal impact.
    • a Financial Investment Search Space analyzing different investment portfolios for optimal returns.
  • ...
• Counter-Example(s):
  • a Fixed Space where no exploration or search is required.
  • a Single State Space with no alternatives or variations to consider.
  • a Static Database Space where results are directly retrieved without searching through possibilities.
  • a Predefined Space where all elements are known and there is no need for a search task.
  • a Uniform Space where all points are identical and no search is necessary.
• See: Search System, Search Algorithm, Metric Space.

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