benjaminjamesbush

👤 Human 👤

Let's run an Interactive Evolutionary Algorithm within this chat interface!

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🤖 ChatGPT 🤖

Sure! An interactive evolutionary algorithm is a type of evolutionary algorithm that allows the user to input parameters or make selections at various points during the optimization process. This can be a fun and interactive way to explore how different parameter choices can affect the performance of the algorithm.

benjaminjamesbush

👤 Human 👤

Hi! I'm Benjamin James Bush. You can call me Ben. Here is an excerpt from my paper, "Hyperinteractive Evolutionary Computation":

Interactive Evolutionary Computation (IEC) is a derivative class of Evolutionary Computation which incorporates interaction with human users. Most IEC applications fall into a category known as “narrowly defined IEC” (NIEC). In NIEC, the task of fitness evaluation is outsourced to human users. For example, a user may be presented with a visual representation of the current generation of individuals. The user is then prompted to provide fitness information about some or all of the individuals. The computer in turn uses this fitness information to produce the next generation of individuals through the application of a predefined sequence evolutionary operators.

We propose Hyperinteractive Evolutionary Computation (HIEC), a novel form of IEC in which a human user actively chooses when and how to apply each of the available evoluti

benjaminjamesbush

👤 Human 👤

Hi! I'm Benjamin James Bush. You can call me Ben. Here is an excerpt from my paper, "Hyperinteractive Evolutionary Computation":

Interactive Evolutionary Computation (IEC) is a derivative class of Evolutionary Computation which incorporates interaction with human users. Most IEC applications fall into a category known as “narrowly defined IEC” (NIEC). In NIEC, the task of fitness evaluation is outsourced to human users. For example, a user may be presented with a visual representation of the current generation of individuals. The user is then prompted to provide fitness information about some or all of the individuals. The computer in turn uses this fitness information to produce the next generation of individuals through the application of a predefined sequence evolutionary operators.

We propose Hyperinteractive Evolutionary Computation (HIEC), a novel form of IEC in which a human user actively chooses when and how to apply each of the avai

benjaminjamesbush

👤 Human 👤

I would like you help me use Hyperinteractive Evolutionary Computation within this chat interface to find a slogan to promote a pizza restaurant. Please initialize the population.

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🤖 ChatGPT 🤖

Sure! I'd be happy to help you use Hyperinteractive Evolutionary Computation (HIEC) to find a slogan for a pizza restaurant. To start, we'll need to initialize the population of potential slogans.

benjaminjamesbush

👤 Human 👤

I would like to use mutation for this round.

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🤖 ChatGPT 🤖

Sure! The mutation operator makes a small, random change to the genetic material of an individual. You can use this operator to introduce new ideas or variations into a slogan.

benjaminjamesbush

👤 Human 👤

Show me the slogans and number them for easy reference. Give me an easy, shorthand format by which I can direct the algorithm as an HIEC user.

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🤖 ChatGPT 🤖

Certainly! Here are the current slogans in the population, numbered for easy reference:

benjaminjamesbush

👤 Human 👤

It looks like you deleted "Pizza that hits the spot every time!" without my permission. Remember, in HIEC, the population size is dynamic. You don't need to delete individuals to make room.

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🤖 ChatGPT 🤖

I apologize for the mistake! I will make sure not to delete any individuals from the population without your permission. In Hyperinteractive Evolutionary Computation (HIEC), the population size is dynamic and can vary over time as you apply evolutionary operators to the population. You don't need to delete individuals to make room for new ones, as you can add as many individuals as you like to the population.

benjaminjamesbush

👤 Human 👤

Great. To create generation 2, I will select a parent and then you will generate 30 children via mutation. The mutation operator is defined as follows:

For an individual x and a mutation rate m, sample a normally distributed random variable with a mean of 0 and a standard deviation of m, then add the sampled value to x.

Use this new mutation operator to create Generation 2, using 0.80070853 as the parent and a mutation rate of 100.

benjaminjamesbush

👤 Human 👤

Show me an example of a list of numbers drawn from a normal distribution with mean 0 and standard deviation 3. Lets start with 30 numbers.

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🤖 ChatGPT 🤖

Here is an example of a list of 30 numbers drawn from a normal distribution with mean 0 and standard deviation 3:

benjaminjamesbush

🤖 ChatGPT 🤖

Here is Generation 1:

Biomorph 1	Biomorph 2	Biomorph 3

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⏳ 22 Biomorphs later... ⌛