<script type="application/ld+json">
{
 "@context": "https://schema.org",
 "@type": "FAQPage",
 "mainEntity": [{
   "@type": "Question",
   "name": "What is a hyper-heuristic?",
   "acceptedAnswer": {
     "@type": "Answer",
     "text": "A hyper-heuristic is a search heuristic that automates the selection, combination, generation, and adaptation of multiple simpler heuristics. It does this to solve complex computational search problems that any of those simpler heuristics could not effectively solve on their own. To achieve it, it often uses machine learning techniques."
   }
 },{
   "@type": "Question",
   "name": "What are the types of hyper-heuristics?",
   "acceptedAnswer": {
     "@type": "Answer",
     "text": "1. Hyper-heuristics to select heuristics.
2. Hyper-heuristics to generate heuristics."
   }
 },{
   "@type": "Question",
   "name": "How are hyper-heuristics different from metaheuristics?",
   "acceptedAnswer": {
     "@type": "Answer",
     "text": "The key difference between hyper-heuristics and metaheuristics is that hyper-heuristics only search within a search space of heuristics, while metaheuristics search within a search space of problems solutions."
   }
 }]
}
</script>

Hyper-heuristic

What is a hyper-heuristic?

A hyper-heuristic is a search heuristic that automates the selection, combination, generation, and adaptation of multiple simpler heuristics. It does this to solve complex computational search problems that any of those simpler heuristics could not effectively solve on their own. To achieve it, it often uses machine learning techniques.

Essentially, hyper-heuristics are high-level automated search methodologies that explore the search space of low-level heuristics or heuristic components to solve those difficult computational search problems.

They seek to reduce the amount of domain knowledge in search methods. The solution chosen or generated should be affordable and easy to implement, without requiring much expertise in heuristics or in the domain in which the problem lies.


What are the types of hyper-heuristics?

There are two types of hyper-heuristics: hyper-heuristics to select heuristics and hyper-heuristics to generate heuristics.

1. Hyper-heuristics to select heuristics

In this type of hyper-heuristics, we provide the hyper-heuristic framework with a set of well-known heuristics that can be used for solving the computational problem in question.

At every stage, a component of the hyper-heuristic called the selection mechanism chooses a heuristic and applies it to an incumbent solution.

Another component of the hyper-heuristic called the acceptance criterion decides whether to accept or reject the solution that was created from the heuristic that was picked by the selection mechanism. If the solution is accepted, it is used to replace the incumbent solution, but if it is rejected, it is discarded. 


2. Hyper-heuristics to generate heuristics

This type of hyper-heuristics focuses on creating new heuristics by using components from existing known heuristics. Like hyper-heuristics to select heuristics, hyper-heuristics to generate heuristics also use a set of known heuristics to start off.

But, unlike the other kind of hyper-heuristics, these ones first decompose the pre-existing heuristics into their basic components, proceeding to select components that can be used to create new heuristics to solve the problem instead of just selecting entire heuristics as they are and using them to solve computational problems.

How are hyper-heuristics different from metaheuristics?

The key difference between hyper-heuristics and metaheuristics is that hyper-heuristics only search within a search space of heuristics, while metaheuristics search within a search space of problems solutions.

Essentially, metaheuristics seek to solve problems directly, while hyper-heuristics seek to find or create an appropriate method or sequence of heuristics that can be used to solve the problem.


What are the applications of hyper-heuristics?

Hyper-heuristics are used in a wide range of problems. Here are some problems and areas in which they are used.

  • Job shop scheduling
  • Personnel scheduling
  • Nurse rostering
  • The traveling salesman problem
  • The maximum cut problem
  • Wind farm layout
  • The boolean satisfiability problem
  • 0-1 knapsack problem
  • Multidimensional knapsack problem

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

About Engati

Engati powers 45,000+ chatbot & live chat solutions in 50+ languages across the world.

We aim to empower you to create the best customer experiences you could imagine. 

So, are you ready to create unbelievably smooth experiences?

Check us out!

Hyper-heuristic

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is a hyper-heuristic?

A hyper-heuristic is a search heuristic that automates the selection, combination, generation, and adaptation of multiple simpler heuristics. It does this to solve complex computational search problems that any of those simpler heuristics could not effectively solve on their own. To achieve it, it often uses machine learning techniques.

Essentially, hyper-heuristics are high-level automated search methodologies that explore the search space of low-level heuristics or heuristic components to solve those difficult computational search problems.

They seek to reduce the amount of domain knowledge in search methods. The solution chosen or generated should be affordable and easy to implement, without requiring much expertise in heuristics or in the domain in which the problem lies.


What are the types of hyper-heuristics?

There are two types of hyper-heuristics: hyper-heuristics to select heuristics and hyper-heuristics to generate heuristics.

1. Hyper-heuristics to select heuristics

In this type of hyper-heuristics, we provide the hyper-heuristic framework with a set of well-known heuristics that can be used for solving the computational problem in question.

At every stage, a component of the hyper-heuristic called the selection mechanism chooses a heuristic and applies it to an incumbent solution.

Another component of the hyper-heuristic called the acceptance criterion decides whether to accept or reject the solution that was created from the heuristic that was picked by the selection mechanism. If the solution is accepted, it is used to replace the incumbent solution, but if it is rejected, it is discarded. 


2. Hyper-heuristics to generate heuristics

This type of hyper-heuristics focuses on creating new heuristics by using components from existing known heuristics. Like hyper-heuristics to select heuristics, hyper-heuristics to generate heuristics also use a set of known heuristics to start off.

But, unlike the other kind of hyper-heuristics, these ones first decompose the pre-existing heuristics into their basic components, proceeding to select components that can be used to create new heuristics to solve the problem instead of just selecting entire heuristics as they are and using them to solve computational problems.

How are hyper-heuristics different from metaheuristics?

The key difference between hyper-heuristics and metaheuristics is that hyper-heuristics only search within a search space of heuristics, while metaheuristics search within a search space of problems solutions.

Essentially, metaheuristics seek to solve problems directly, while hyper-heuristics seek to find or create an appropriate method or sequence of heuristics that can be used to solve the problem.


What are the applications of hyper-heuristics?

Hyper-heuristics are used in a wide range of problems. Here are some problems and areas in which they are used.

  • Job shop scheduling
  • Personnel scheduling
  • Nurse rostering
  • The traveling salesman problem
  • The maximum cut problem
  • Wind farm layout
  • The boolean satisfiability problem
  • 0-1 knapsack problem
  • Multidimensional knapsack problem

Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

Share

Continue Reading