# Search Space

Jump to navigation
Jump to search

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.

- ...

- Algorithmic Search Spaces, such as:
**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.