Biogeography-based optimization | Wikipedia audio article
Channel: wikipedia tts
Date: 2019-03-28

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Biogeography-based_optimization


00:01:59 1 Underlying principles
00:10:06 2 Algorithm
00:15:30 3 Discussion of the BBO algorithm
00:17:50 4 Algorithmic variations
00:23:13 5 Hybridization
00:23:58 6 Software
00:24:08 6.1 MATLAB
00:24:48 6.2 R
00:25:07 7 Extensions
00:26:00 8 Mathematical analyses
00:26:20 9 Applications



Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.7268783367925833
Voice name: en-US-Wavenet-E


"I cannot teach anybody anything, I can only make them think."
- Socrates


SUMMARY
=======
Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) that optimizes a function by stochastically and iteratively improving candidate solutions with regard to a given measure of quality, or fitness function. BBO belongs to the class of metaheuristics since it includes many variations, and since it does not make any assumptions about the problem and can therefore be applied to a wide class of problems.
BBO is typically used to optimize multidimensional real-valued functions, but it does not use the gradient of the function, which means that it does not require the function to be differentiable as required by classic optimization methods such as gradient descent and quasi-newton methods. BBO can therefore be used on discontinuous functions.
BBO optimizes a problem by maintaining a population of candidate solutions, and creating new candidate solutions by combining existing ones according to a simple formula. In this way the objective function is treated as a black box that merely provides a measure of quality given a candidate solution, and the function's gradient is not needed.
Like many EAs, BBO was motivated by a natural process; in particular, BBO was motivated by biogeography, which is the study of the distribution of biological species through time and space. BBO was originally introduced by Dan Simon in 2008.