An Introduction to Hill Climbing Algorithm in AI

In AI, machine learning, deep learning, and machine vision, the algorithm is the most important subset. With the help of these algorithms, (What Are Artificial Intelligence Algorithms and How Do They Work, n.d.) the computer, system, or the model becomes able to understand what kind of information the user wants to process and what kind of results the user wants after understanding some work from the surrounding as well. These algorithms are very important in AI as on the basis of these algorithms the computer or model will be trained accordingly and will be able to train the data that has been provided to it. The most common example of these algorithms can be found in Alexa, Siri, or Google Home. The more a person or a user interacts with them, their search can become better as they can perceive the output from the environment, and mood of the user by observing their song collection, interests, likes, and dislikes as well. With the help of these factors, the observation of these agents becomes so much strong as they interact with the user more frequently. So, it becomes obvious that they have very strong algorithms installed which help them to train on their own. There are certain algorithms that are very important and are frequently used; random forest, logic regression, Naïve Bayes, and Artificial Neural Networks. (Top 6 AI Algorithms In Healthcare, n.d.)

 

 

The most common algorithm in AI to solve mathematical problems is the hill-climbing algorithm (Understanding Hill Climbing Algorithm in Artificial Intelligence | Engineering Education (EngEd) Program | Section, n.d.). This problem can be used in job scheduling, marketing, ad development, and predictive maintenance as well. It is a heuristic technique, or in simpler words hill climbing is basically a search technique or informed search technique having different weights based on real numbers assigned to different nodes, branches, and goals in a path. Now on the basis of these numbers and the heuristic defined in the AI model the search can become better. The main feature associated with the hill-climbing algorithm is its large input efficiency and better heuristic assignment. 

 

 Understanding Hill Climbing Algorithm Visually
Figure 1. Understanding Hill Climbing Algorithm Visually (Introduction to Hill Climbing | Artificial Intelligence – GeeksforGeeks, n.d.)

 

From figure 1 it becomes obvious that the hill-climbing algorithm depends on the two components one is the objective function, and the other is state space. The current state is the state of the search in which the agent presently stands. A local maximum is another goal-oriented solution, but it is not the optimized search result. To achieve a better result the model must have achieved a global maximum point for better accuracy and precision. (Introduction to Hill Climbing | Artificial Intelligence – GeeksforGeeks, n.d.). A brief introduction of each of the points shown in the above graph is as follows. 

  • Local Maximum is a state as discussed earlier which is obviously better than the current state but there is a better state available as compared to the local maximum in the system. 
  • Global Maximum, as shown in the graph, is the best state and no state gets better than this state. 
  • Ridge is the region that is higher than its neighbor but has a slope steeping downward.
  • The current state as observed by its name is a state in which the agent is currently settled or examined the present state.
  • The shoulder is a point on the uphill. (Skiena, 2010)

 

 

There are many types of such Algorithms a few of them are defined as follows. 

 

Simple Hill Climbing 

 

The working of this kind of hill climbing is kind of very simple. It collects the data from the neighbors of the current node and examines each node. With the help of this simple exercise, the current cost of the next upcoming cost can be optimized, and hence minimal time has been consumed. 

 

Steepest Ascent Hill Climbing

 

It is a type of hill-climbing algorithm, but it is better than the simplest one. It also examines all the neighboring nodes like in the previous techniques, but it gives weights or heuristics to the neighboring nodes and based on the technique of the least-cost solution finds the shortest path for the goal and achieves the goal by that technique. They examine those nodes which are close to the solution. 

 

Stochastic Hill Climbing

 

It is completely opposite to the techniques which are discussed earlier. In this technique, the agent doesn’t find the values of the neighboring nodes. It selects the neighboring nodes completely randomly and goes on that node and on the basis of the heuristic of that specific node the agent then examines whether to continue this path or not. (Russell & Norvig, 2003)

 

 

A few advantages of hill-climbing are as follows. (Hill Climbing in Artificial Intelligence | Types of Hill Climbing Algorithm, n.d.)

  • It is a very useful technique while solving problems like job searching, salesman techniques, chip design, and management.
  • When a user has very limited computational power, he can use this technique in order to get better results. No external ram or cloud computing is required for using such technology as it requires very less computational power. 
  • The agent moves in the direction of the goal which optimizes our cost.
  • This algorithm has provided feedback to the model on the basis of which the system gets better from time to time
  • No backtracking occurred using such an algorithm.

 

 

There are several disadvantages associated with hill climbing as well. A few of them are listed as follows. (Hill Climbing in Artificial Intelligence | Types of Hill Climbing Algorithm, n.d.)

  • The efficiency and effectiveness get compromised while using this technique.
  • If the value of the heuristic is uncertain then this technique is not recommended. 
  • It is an immediate solution, not an effective solution.
  • The results obtained from this technique are uncertain and are not reliable. 

 

References

 

  • Hill Climbing in Artificial Intelligence | Types of Hill Climbing Algorithm. (n.d.). Retrieved February 27, 2022, from https://www.educba.com/hill-climbing-in-artificial-intelligence/
  • Introduction to Hill Climbing | Artificial Intelligence – GeeksforGeeks. (n.d.). Retrieved February 27, 2022, from https://www.geeksforgeeks.org/introduction-hill-climbing-artificial-intelligence/
  • Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. In Artificial Intelligence A Modern Approach (2nd ed.). Prentice-Hall. http://aima.cs.berkeley.edu/
  • Skiena, S. S. (2010). The Algorithm Design Manual (2nd ed.). Springer Science+Business Media.
  • Top 6 AI Algorithms In Healthcare. (n.d.). Retrieved February 27, 2022, from https://analyticsindiamag.com/top-6-ai-algorithms-in-healthcare/
  • Understanding Hill Climbing Algorithm in Artificial Intelligence | Engineering Education (EngEd) Program | Section. (n.d.). Retrieved February 27, 2022, from https://www.section.io/engineering-education/understanding-hill-climbing-in-ai/
  • What are Artificial Intelligence Algorithms and How do they work. (n.d.). Retrieved February 27, 2022, from https://rockcontent.com/blog/artificial-intelligence-algorithm/

 
 
Neeraj Agarwal is a founder of Algoscale, a data consulting company covering data engineering, applied AI, data science, and product engineering. He has over 9 years of experience in the field and has helped a wide range of organizations from start-ups to Fortune 100 companies ingest and store enormous amounts of raw data in order to translate it into actionable insights for better decision-making and faster business value.
 

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