The "memetic algorithms" of Professor Pablo Moscato are taking the world by storm

Professor Pablo Moscato said his so-called “memetic algorithms” have vast real-world applications.

His invention, which he’s been developing for more than 30 years, has been listed among science’s hottest research fields.

It now sits alongside the likes of lithium batteries, carbon nanotubes and next-generation DNA sequencing.

Professor Moscato, who grew up in the Third World in Argentina, came up with the term “memetic”, after reading about “memes” in a book titled The Selfish Gene, by Richard Dawkins.

Memes are ideas and behaviours that spread – which is what his method has done.

The professor broke boundaries and challenged orthodoxy in creating his new computing paradigm.

In traditional computing, there’s “always one algorithm working to solve a problem”. 

An algorithm is essentially a set of rules to be followed to solve a problem.

“My method uses a collection of algorithms. These algorithms are competing and co-operating like a team to create new solutions.

“What I created is like an ecology, where algorithms can be killing each other and creating new algorithms.”

While this may be difficult for many to understand, he said this idea had similarities to scenes in the film Matrix Reloaded, in which agents replicate themselves.

Simply put, his method is based on the idea that “when you have a problem, you have several ways of solving it”.

“But instead of using only one way, why not harness the power of several different ideas? You combine them.”

Professor Moscato initially faced resistance to his method from the mathematics establishment, but he’s not one to be bound by the rigid thinking of the status quo.

“There was resistance at the time. People thought, why not just do a better single algorithm? Why do we need to be working with many? The reason is because many algorithms outsmart single ones.”

His work, which combines mathematical models and computer algorithms, is now being used to tackle the most challenging biotechnology problems.

It’s being used in personalised medicine, which involves targeted treatments and therapies to individuals based on their genes. 

“For example, when you want to identify the molecular profile of 2000 women with breast cancer, most researchers hire a PhD student,” he said.

“They use the first algorithm that’s available to find a solution.

“But they can use my method, which is more powerful. It can be used to guarantee that a solution, in some cases, is optimal. It can provide very good solutions very quickly.”

The method has already made progress for research into cancer, Alzheimer's disease, multiple sclerosis and childhood epilepsy.  

The method was originally used in logistical problems in the mid-90s. For example, it was used to create optimal timetables for the National Hockey League in the US and Canada.

It was also used for timetables in schools and universities in the UK, transportation in Argentina and production planning in various industries in Brazil.

His published work on memetic algorithms has gained worldwide attention with more than 10,000 citations [references in academic papers].

Initially there was a “wave of interest from Europe”, which has now spread to Asia, including China, Japan and Singapore.

“It’s been growing exponentially. In China, there’s a research paper every other day for applications. The applications are absolutely everywhere,” he said.

His work was recently recognised at the Rotary Humanitarian STAR (Science, Technology, Aerospace, Robotics) Awards.

It was nominated for its humanitarian benefit in the categories of “health and medical” and “knowledge sharing”.

In a book chapter on the untold story of memetic algorithms, Professor Moscato wrote that: “Their time in history is right now”.

“They have always been aimed at bringing the best from each field of expertise.” 

He’s looking forward to observe their development over the next 30 years.