Tech Content
8 minutes

Heuristics are nothing new, they play an important role in our daily lives, in both problem-solving and data-driven decision-making (DDDM). As nowadays, the world is full of information, and our brains are only capable of processing a certain amount of it, heuristics help us a lot in this sense. Because if you would try to analyze every single aspect of every situation or decision, you would never get anything done.

We take thousands of decisions every single day and most of them we don’t really think about, we “know” how to behave in certain situations based on our experience and that’s what heuristics are about. When we are trying to solve a problem or make a decision, we often turn to these mental shortcuts when we need a quick solution.

Introduction to Heuristics

In the world of computer science, heuristics play a vital role in tackling complex problems and providing efficient solutions. This introductory section will explore the concept of heuristics and highlight their significance in problem-solving.

Definition of Heuristics

Heuristics is practical problem-solving techniques or methods used to find quick and effective solutions when dealing with complex issues. They are only sometimes guaranteed to produce the best or optimal solution, but they often lead to satisfactory results within a reasonable time frame. Heuristics are particularly useful when dealing with problems that have incomplete, uncertain, or imprecise information or when the search space is too ample for a complete, exhaustive search.

Importance of Heuristics in Problem Solving

The importance of heuristics in problem-solving is evident in their versatility and adaptability across different computer science domains. Some of the reasons why heuristics are crucial for problem-solving include:

  1. Efficiency: Heuristics can significantly reduce the time and resources needed to solve problems, especially when the search space is enormous, or the problem is too complex.
  2. Simplicity: Heuristic methods are often easier to implement and understand than complicated algorithms, making them more accessible to a broader audience.
  3. Flexibility: Heuristics can be applied to various problem domains, often with minor modifications, making them versatile problem-solving tools.
  4. Robustness: In situations where the available data is incomplete, uncertain, or noisy, heuristics can still provide helpful solutions, making them applicable to real-world problems.

History of Heuristics in Computer Science

Heuristics have been crucial to the growth and advancement of computer science. Here's how the history of heuristics and how they have changed over time.

Early Beginnings

Ancient Greek mathematicians and philosophers used heuristic techniques to solve mathematical and logical issues, giving rise to heuristic usage. 

Yet, with the development of artificial intelligence (AI) research in the 1950s and 1960s, heuristics were formally introduced in computer science for the first time.

Alan Turing, one of the early AI pioneers, first suggested a heuristic search in his landmark article "Computing Machines and Intelligence." The exploration of heuristics as a tool to solve challenging problems in AI and computer science was made possible thanks to Turing's work.

George Pólya, another important person, wrote the famous book "How to Solve It" in 1945. It offered a collection of heuristics for resolving mathematical issues. Many computer scientists and researchers were motivated by Pólya's efforts to

Evolution of Heuristic Techniques

Over the years, heuristic techniques have evolved and diversified to address various complex problems in computer science. Some of the most notable developments in the history of heuristics include:

  1. Game Theory: In the 1950s and 1960s, researchers like Claude Shannon and Arthur Samuel developed early heuristics to explore optimal game strategies like chess and checkers. Their work paved the way for more advanced heuristic techniques used in game theory today.
  2. Optimization: In the 1970s, researchers began developing metaheuristic optimization algorithms, such as genetic and simulated annealing, to find near-optimal solutions to complex optimization problems.
  3. Machine Learning: The 1980s and 1990s witnessed significant advancements in machine learning techniques, such as decision trees and neural networks, which rely on heuristic methods to learn from data and make predictions.
  4. Human-Computer Interaction: Heuristic evaluation, a method for identifying usability issues in user interfaces, was introduced by Jakob Nielsen in the 1990s, highlighting the application of heuristics in human-computer interaction.
  5. As computer science continues to evolve, so do heuristic techniques, with researchers constantly developing new and innovative ways to apply heuristics to tackle increasingly complex problems.


So, What is Heuristic Programming?

Heuristics are mental shortcuts that help us make decisions and judgments quickly without having to spend a lot of time researching and analyzing information. They usually involve focusing on one aspect of a complex problem and ignoring others. They work well under most circumstances, but they can lead to systematic deviations from logic, probability, or rational choice. Examples that employ heuristics include using a rule of thumb, an educated guess, an intuitive judgment, a guesstimate, stereotyping, profiling, or common sense.

Heuristics in Software Development

Heuristics have become indispensable to software engineering, helping developers create more efficient, maintainable, and user-friendly software. This section will explore various ways heuristics are employed in software development.

Design Patterns

Design patterns are reusable solutions to common problems that arise in software design. They are heuristics that software developers can apply when faced with specific design challenges. By leveraging these well-established patterns, developers can create more maintainable and scalable software while avoiding common pitfalls.

Some widely used design patterns include:

  1. Singleton: Assures a class has just one instance and offers a universal access point to that instance.
  2. Factory Method: Establishes a framework for instantiating objects in a superclass, allowing subclasses to select the objects they want to use.
  3. Observer: Establishes a one-to-many relationship between objects so that all of its dependents are automatically informed and updated when one changes.

Agile Development

Agile development methodologies, such as Scrum and Kanban, have become famous for managing software projects more efficiently in recent years. These methodologies incorporate heuristics to streamline development, improve collaboration, and deliver high-quality software.

Some critical heuristics in agile development include:

  1. Iterative Development: Agile methodologies promote short development cycles, allowing teams to gather feedback continuously, identify issues, and adjust their plans accordingly.
  2. Time-boxing: Setting strict time limits for tasks helps teams maintain focus and prevents them from getting bogged down in unnecessary details.
  3. Daily Stand-ups: Regular short meetings enable team members to share updates, identify roadblocks, and ensure everyone is on the same page.
  4. Prioritization: Agile methodologies prioritize tasks based on their value to the end user, ensuring that the most critical features are delivered first.

Code Smells and Refactoring

Code smells indicate potential problems in the source code, often resulting from poor design choices or programming practices. Heuristics can be used to identify these code smells and guide developers in refactoring their code to improve its maintainability, readability, and overall quality.

Some examples of code smells, and corresponding refactoring techniques include:

  1. Long Method: A method that is too long and difficult to understand can be broken down into smaller, more manageable processes.
  2. Duplicate Code: Identical or similar code in multiple locations can be consolidated into a single reusable method or class.
  3. Feature Envy: When a method accesses data from another class more than its data, moving the technique to that class may be more appropriate.
  4. Software engineers can produce more effective, maintainable, and user-friendly software that satisfies the requirements of their customers and end users by utilizing heuristics in the development process.


Heuristics in Computer Sciences

In computer science, a heuristic is a problem-solving strategy or method that is not guaranteed to find the optimal solution, but is designed to find a satisfactory solution in a reasonable amount of time. Heuristics are often used in artificial intelligence, search algorithms, and optimization problems where it is not possible or impractical to use an algorithm that guarantees a correct solution.

Heuristic Algorithm


Primary Use Cases


Greedy Algorithm

A simple approach that makes the locally optimal choice at each step in the hopes of finding a globally optimal solution.

Resource allocation, task scheduling, routing problems

May not always find the global optimum, can get stuck in local optima

Hill Climbing

An iterative optimization algorithm that starts with an initial solution and incrementally improves it by making small adjustments.

Function optimization, feature selection, game playing

Can get trapped in local optima, highly sensitive to initial solution

Simulated Annealing

A probabilistic optimization algorithm inspired by the annealing process in metallurgy, which allows occasional non-optimal moves to escape local optima.

Combinatorial optimization, scheduling, traveling salesman problem

Slower convergence, requires careful tuning of parameters

Genetic Algorithm

A search heuristic based on the principles of natural selection and genetic inheritance, which evolves a population of candidate solutions to find an optimal solution.

Function optimization, machine learning, scheduling

Computationally expensive, may require many iterations

Tabu Search

A metaheuristic that explores the solution space by moving from one solution to another, using memory structures to avoid revisiting previously encountered solutions.

Combinatorial optimization, vehicle routing, job-shop scheduling

Requires careful tuning of parameters, can be computationally intensive

Heuristics are often based on experience, intuition, or common sense and are used to guide the search for a solution in a way that is efficient and effective. They can be useful in situations where the problem is too complex or too large to be solved by an algorithm that guarantees a correct solution.

Examples of heuristics in computer science include:

  • Best-first search
  • Hill climbing
  • Simulated annealing
  • Genetic algorithms

Heuristics can be very useful in computer science, they can be faster and more efficient than other techniques, but they are also less reliable and might not provide optimal solutions.

In computer science, artificial intelligence, and mathematical optimization, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. 

Heuristic programming employs a practical method, not guaranteed to be optimal, perfect, logical, or rational, but instead sufficient for reaching an immediate goal. It is important to highlight that Heuristics are the strategies derived from previous experiences with similar problems. These strategies rely on using readily accessible, though loosely applicable, information to control problem-solving in human beings, machines, and abstract issues. 
And the objective of a heuristic is to produce a solution in a reasonable time frame that is good enough for solving the problem at hand. 

The trade-off criteria for deciding whether to use a heuristic for solving a given problem:

  • Optimality: When several solutions exist for a given problem, does the heuristic guarantee that the best solution will be found? Is it actually necessary to find the best solution?
  • Completeness: When several solutions exist for a given problem, can the heuristic find them all? Do we actually need all solutions? Many heuristics are only meant to find one solution.
  • Accuracy and precision: Can the heuristic provide a confidence interval for the purported solution? Is the error bar on the solution unreasonably large?
  • Execution time: Is this the best-known heuristic for solving this type of problem? Some heuristics converge faster than others. Some heuristics are only marginally quicker than classic methods.

And now, the main question is: why do we rely on heuristics? 
Psychologists have suggested a few different theories:

  • Effort reduction: According to this theory, people utilize heuristics as a type of cognitive laziness. Heuristics reduce the mental effort required to make choices and decisions.
  • Attribute substitution: Other theories suggest people substitute simpler but related questions in place of more complex and difficult questions.
  • Fast and frugal: Still other theories argue that heuristics are actually more accurate than they are biased. In other words, we use heuristics because they are fast and usually correct.

This is in contrast to algorithmic programming, which is based on mathematically provable procedures. But what is important to understand here is that Heuristic programming is characterized by programs that are self-learning; they get better with experience. Remember, heuristics don't guarantee the best or perfect solution, but they often lead to good solutions in a reasonable time frame, which is why they're so widely used in computer science.

Let us know about your thoughts and experience with heuristic programming, we would be happy to discuss it in the comments section below.