Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment. It only takes a minute to sign up.
Sign up to join this community
In the article Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (DOI 10.1007/s00521-015-1870-7), it's written
The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces
where MVO stands for Multi-Verse Optimizer.
What does "unknown search spaces" mean in the context of Evolutionary Algorithms and, especially, in the context of the Multi-Verse Optimizer?
(Disclaimer: although I have never seen a formal definition of unknown search space, here is my attempt to define it based on my knowledge of search and search algorithms in machine learning and evolutionary algorithms; I am aware of a definition of unknown environment (see chapter 2, p. 44, of Norvig and Russell's AI book), but that definition is different from the one below.)
An unknown search space is a search space (i.e. a set of objects of interest with some relationship between them) where you do not know anything about
A typical example where the search space is known is the search space associated with a path-finding problem where you know the start and goal locations, the intermediate locations that you can go through from the start to the goal locations, and the distance between locations. If you have this information, you can find the optimal path from the start to the goal location by just exploiting your knowledge of it (you can use e.g. depth-first search). Think about finding the shortest path from one city to another.
In other (most) cases, you may not know anything about the relationship between 2 or more solutions/objects, unless you explore the search space (i.e. take random actions or some other more informed actions).
For example, let's suppose that you want to search for a function of the form $f : [0, 1]^{28 \times 28} \rightarrow \{0, 1\}$ from a set of functions $\mathbb{F}$, i.e. $f \in \mathbb{F}$. In this case, you can think of a function $y = f(\mathbf{x}) \in \mathbb{F}$ as a classifier of greyscale images, where $\mathbf{x} \in [0, 1]^{28 \times 28}$ is a $28 \times 28$ greyscale image and $y \in \{0, 1\}$ is the class (name) of the object in the image $\mathbf{x}$ (in this case, we assume there's only one main object in the image $\mathbf{x}$). In machine learning, $\mathbb{F}$ could be represented by a set of neural networks (with a specific architecture, e.g. number of layers), and $f \in \mathbb{F}$ would be one of these specific configurations. In this context, usually, we do not know anything about the relationship between these neural networks, so the search space is unknown. So, you need to explore the search space in some way, for example, by changing some of the parameters of one neural network $f \in \mathbb{F}$ to get another neural network $f' \in \mathbb{F}$ and see how that affects, for example, your accuracy at classifying images.
This definition also applies to evolutionary algorithms (and, although I have not read that paper, I assume it also applies to that context). In fact, evolutionary algorithms are usually used to solve problems with unknown search spaces. For example, test case selection or finding a policy for an agent.
