biology

Learnable evolution model

Learnable evolution model is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Learnable evolution model rather than just read about it. In short: The learnable evolution model (LEM) is a non-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals (candidate problem solutions). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutations and/or recombinations), LEM employs hypothesis generation and i…

Key takeaways

Learnable evolution model belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Learnable evolution model to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Learnable evolution model from memory before moving on to harder problems.

Reference excerpt

The learnable evolution model (LEM) is a non-Darwinian methodology for evolutionary computation that employs machine learning to guide the generation of new individuals (candidate problem solutions). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutations and/or recombinations), LEM employs hypothesis generation and instantiation operators. The hypothesis generation operator applies a machine learning program to induce descriptions that distinguish between high-fitness and low-fitness individuals in each consecutive population. Such descriptions delineate areas in the search space that most likely contain the desirable solutions. Subsequently the instantiation operator samples these areas to create new individuals. LEM has been modified from optimization domain to classification domain by augmented LEM with ID3 (February 2013 by M. Elemam Shehab, K. Badran, M. Zaki and Gouda I. Salama).

Selected references Cervone, P.; Franzese (January 2010), "Machine Learning for the Source Detection of Atmospheric Emissions", Proceedings of the 8th Conference on Artificial Intelligence Applications to Environmental Science, Code J1.7 Wojtusiak, J.; Michalski, R. S. (2006), "The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems", Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, WA, p. 1281, CiteSeerX 10.1.1.72.2298, doi:10.1145/1143997.1144197, ISBN 978-1595931863, S2CID 6133889 {{citation}}: Cite uses deprecated parameter |citeseerx= (help)CS1 maint: location missing publisher (link) Wojtusiak, J. (July 8–12, 2006), "Initial Study on Handling Constrained Optimization Problems in Learnable Evolution Model", Proceedings of the Graduate Student Workshop at Genetic and Evolutionary Computation Conference, GECCO 2006 Jourdan, L.; Corne, D.; Savic, D.; Walters, G. (2005), "Preliminary Investigation of the 'Learnable Evolution Model' for Faster/Better Multiobjective Water Systems Design", Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, vol. 3410, pp. 841–855, CiteSeerX 10.1.1.73.9653, doi:10.1007/978-3-540-31880-4_58, ISBN 978-3-540-24983-2 {{citation}}: Cite uses deprecated parameter |citeseerx= (help) Domanski, P. A.; Yashar, D.; Kaufman, K.; Michalski, R. S. (April 2004), "An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model", International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research, 10: 201–211 Kaufman, K.; Michalski, R. S. (2000), "Applying Learnable Evolution Model to Heat Exchanger Design", Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000): 1014–1019 Cervone, G.; Michalski, R. S.; Kaufman, K. A. (July 2000). "Experimental validations of the learnable evolution model". Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). Vol. 2. pp. 1064–1071. doi:10.1109/CEC.2000.870765. ISBN 0-7803-6375-2. S2CID 3149132. Michalski, R. S. (2000), "LEARNABLE EVOLUTION MODEL Evolutionary Processes Guided by Machine Learning", Machine Learning, 38: 9–40, doi:10.1023/A:1007677805582 Michalski, R .S. (June 11–13, 1998), "Learnable Evolution: Combining Symbolic and Evolutionary Learning", Proceedings of the Fourth International Workshop on Multistrategy Learning (MSL'98): 14–20 H Yar, M. (June 11–13, 2016), "A survey on evolutionary computation: Methods and their applications in engineering", Mod. Appl. Sci: 14–20

Worked examples

Example 1 — a first encounter with Learnable evolution model

Start with the simplest possible case. Write down what Learnable evolution model claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Learnable evolution model before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Learnable evolution model ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Learnable evolution model

In research: Learnable evolution model appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Learnable evolution model in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Learnable evolution model is common in secondary-school and first-year university syllabi. It links to neighbouring topics Evolutionary computation, so understanding it makes those chapters shorter.
In everyday life: Look for Learnable evolution model outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Learnable evolution model in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Learnable evolution model means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Learnable evolution model out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Learnable evolution model in simple terms?

Why does Learnable evolution model matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Learnable evolution model?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Learnable evolution model.