Interactive Evolutionary Computation and Large Language Models: Experimenting with ChatGPT
Abstract: I explore the potential of using ChatGPT as a platform for Interactive Evolutionary Computation (IEC) through a series of experiments. The natural language interface allowed me to incorporate advanced IEC features on the fly, such as Online Knowledge Embedding and subpopulations, as well as innovative features, such as context dependent and generalized evolutionary operators. I demonstrate the importance of supplying ChatGPT with context, improving its ability to intelligently collaborate with the user. I discuss ChatGPT’s limitations related to numbers, graphics, memory, and response length, and speculate on how these limitations might be overcome. Finally, I discuss the relationship between IEC and ChatGPT in terms of the Japanese concepts of Kansei and Chisei.
Keywords: ChatGPT, Interactive Evolutionary Computation, Large Language Models, Active User Intervention, Kansei, Chisei.
ChatGPT is a state-of-the-art large language model (LLM) implemented as an AI chatbot that can generate human-like responses [1]. Examples of its use can be found all over social media, highlighting the wide range of tasks it can handle. From drafting short stories to designing Python functions, creating song lyrics to writing blog posts (*wink wink*), ChatGPT has demonstrated its versatility for a variety of generative language tasks. One particular example that caught my attention was its ability to generate slogans.
As graduate students, my colleagues and I developed SemantiStorm [2], an electronic brainstorming system that utilized interactive evolutionary computation (IEC) [3]. Our human subject experiments involved groups of students working together to create marketing slogans promoting the sale of a laptop. Could ChatGPT be used in a similar way, to generate slogans through a simulated evolutionary process?
Generating Slogans as an Evolutionary Process
The traditional approach to generating slogans using ChatGPT is to simply ask it to produce a list of slogans. You can then select a suitable slogan from the list. If no suitable slogan was generated, you can ask ChatGPT to generate additional slogans. However, ChatGPT is rare among large language models in that it is particularly skilled at responding to follow-up requests. For example, if one of the generated slogans is promising but could be improved, you can ask ChatGPT to create alternate versions of that slogan. Alternatively, if you are having trouble deciding between two promising slogans, you can ask ChatGPT to generate a slogan that combines elements from both. This process can be iterated over several cycles until a high-quality slogan is found.
Those who are familiar with the theory of evolution may have noticed the parallels that exist between the above process of forming slogans with ChatGPT and evolutionary processes that have shaped all life on Earth. See the table below, inspired by [4], for examples of this correspondence.
Upon noticing these parallels, it occurred to me that it might be possible to structure my interaction with ChatGPT explicitly as an interactive evolutionary algorithm (IEA).
Experiment 1: My First ChatGPT-Based IEA
My early attempts to structure my interaction with ChatGPT as an IEA went surprisingly well. With a little tinkering, I created the following prompt:
Overall, the IEA performed almost exactly as expected. In response to my simple commands, the system was able to generate new slogans from scratch or remove unwanted slogans, as well as mutate and recombine existing slogans to create new ones. After each command, the system correctly displayed an updated population. This style of IEC, in which the user controls when and how each evolutionary operator is used, is an example of Hyperinteractive Evolutionary Computation [5]. See the control flow diagram below.
My experiment demonstrated that ChatGPT can indeed run an IEA. More generally, this shows that ChatGPT is capable of following logical instructions, and sparks curiosity about ChatGPT's computational abilities (thanks to @HirokiSayama for this observation).
Rapid Development, Hyper-Readable Specification
I was struck by how quickly everything came together: I developed an IEA in less than ten minutes, using only natural language to briefly define the population, the evolutionary operators, and my desired pattern of user interaction.
The readability of the algorithm's natural language specification is unparalleled. Even famously readable programming languages, such as Python, can't compete with the level of accessibility that ChatGPT provides.
The potential for rapid development and the ability to use hyper-readable algorithm specifications makes this approach to IEC an intriguing option for educational settings. Students may have a wide range of backgrounds and expertise, and there is a need to cover a large amount of material in a short period of time.
Active User Intervention
As I continued playing with the system, a problem emerged. After multiple recombinations, one of the generated slogans was too long, despite my prompt specifying that all slogans should be of a similar length. However, the issue was easily resolved by prompting ChatGPT to shorten it. See the transcript below.
This “fix” demonstrates the ability to issue quick one-off commands on the fly, a powerful feature that is lacking in most traditional IEAs. This also demonstrates the flexibility that is afforded when natural language is used to communicate with an IEA. It allows the user to adjust the algorithm as it runs. On a user experience level, this flexibility allows the user to "break out" from the confines of an IEA's generational iteration, increasing the user's sense of control [6], and cementing them as the initiator of action, rather than simply a respondent [7]. In the IEC literature, such features fall under the term "Active User Intervention" [8]. I will provide several more examples showcasing the flexibility of ChatGPT-based IEAs throughout this article.
Online Knowledge Embedding
Online Knowledge Embedding [9] is an Active User Intervention available in some IEC systems which allows the user to submit new constraints to the evolving population as the algorithm is running. For example, if the task is to evolve a face, a user may at some point decide that the final result should have purple eyes. With Online Knowledge Embedding, the user can narrow the search space such that all future generations will only contain faces with purple eyes. See the figure below.
I was curious to see if I could use Online Knowledge Embedding to constrain the space of slogans that I was evolving with ChatGPT. I issued a one-off command instructing ChatGPT to convert all the slogans in the population to slogans that referenced play or gaming, and to include references to play or gaming in all of the slogans it created in the future. ChatGPT behaved as requested, establishing that Online Knowledge Embedding is possible within ChatGPT-based IEC. See the transcript below.
Subpopulations
Some IEC systems, such as Unemi’s SBArt, allow users to evolve multiple subpopulations simultaneously [10]. Just as real-world groups of organisms often diverge when their subpopulations are isolated from each other, so too do evolving subpopulations within an IEC system tend to diverge. Users can use subpopulations to simultaneously explore different approaches to solving a problem, or to simultaneously solve two slightly different problems. I asked ChatGPT to split my population into two subpopulations, one for laptop slogans related to gaming, and the other for laptop slogans related to productivity:
As I was preparing to use the old “generate” operator to create more slogans, I wasn’t sure what to expect. How would ChatGPT handle the creation of new slogans within each subpopulation? To my surprise, the generate operator was able to take the theme of each subpopulation into account. ChatGPT understood the purpose of each subpopulation and had tailored the functionality of the generate operator accordingly.
ChatGPT had, in essence, upgraded the generate operator to be context dependent, respecting the constraints imposed by a parallel form of Online Knowledge Embedding. Context dependent operators are a common feature in “Island Model” evolutionary algorithms [11], but this might be the first time such a feature has been part of an Interactive Evolutionary Algorithm (please let me know in the comments if I am mistaken). The fact that this feature was added automatically, seemingly because ChatGPT determined that it was the appropriate thing to do, was particularly impressive, and highlights the potential of large language models in the field of IEC.
So far, my two subpopulation system was behaving like two isolated IEAs running in parallel. However, as with Island Model evolutionary algorithms, the recombination of individuals from different subpopulations can sometimes yield good results. I recombined the slogans from my “Gaming” and “Productivity” subpopulations to create a third subpopulation, called “Both”.
This method for creating the “Both” subpopulation demonstrates how an individual level operator, such as recombination, can easily be generalized to work at the subpopulation level, through a simple natural language command. I believe subpopulation level operator generalizations constitute a new innovation within the field of IEC.
*Interactive* Interactive Evolutionary Algorithm Development
This version of the ChatGPT-based evolutionary algorithm was no simple automaton. This was a thoughtful and intelligent collaborator that understood the meaning of the process it was being asked to carry out. Or, if not true understanding, then at least an effective illusion, at times.
In my first experiment, I was able to successfully create a working IEA within ChatGPT by specifying the population, evolutionary operators, and desired pattern of interaction using natural language. This demonstrated the potential of using large language models for IEC. However, it is important to note that this approach is similar to traditional IEA development in that the full specification of the algorithm was completely conceived and specified by a human (me) before interaction with ChatGPT began. But this approach ignores some of ChatGPT’s greatest strengths as a collaborative AI assistant. What if instead of telling ChatGPT exactly how to run an IEA, we work with ChatGPT to develop one instead?
Experiment 2: Letting ChatGPT take the lead
I asked ChatGPT about its familiarity with IEC. It responded confidently:
I have been trained on a wide range of topics, including Interactive Evolutionary Computation (IEC).
— ChatGPT
Indeed, ChatGPT has enough working knowledge of IEC to implement an IEA all by itself, and will do so within the chat interface, if asked:
When ChatGPT asked me for feedback on its proposed algorithm, I should have perhaps taken it up on its offer, because the “default experience” provided by ChatGPT was not great. I was given the tedious task of providing explicit numerical fitness scores for each slogan. I could only bring myself to type out three scores per generation. The algorithm chugged along, but then abruptly converged after only four generations. Still, the fact that ChatGPT was able to whip up a full IEC application, with but a simple request, is provocative.
Taking back control
Having just witnessed a pizza slogan “population collapse”, I began attempting “conservation efforts”. I ordered ChatGPT to:
- Bring some of the older slogans back into the population.
- Shift to a “steady state” architecture [12] so that only one slogan is created at a time.
- Stop deleting slogans altogether, until further notice.
It took some coaxing, but I was eventually able to reestablish a healthy, stable population of slowly evolving pizza slogans to continue my experiment. In doing so, I demonstrated that ChatGPT-based IEC is able to completely change its architecture on the fly. As far as I am aware, this kind of flexibility is a novel development within the field of IEC.
Experiment 3: “Here, Read my Paper”
I was moved, having witnessed such an appropriate and human-like response from a machine.
Providing context is important when working with large language models, perhaps even more so than when working with other humans, who can use their knowledge, experience, understanding of the world, as well as situational and nonverbal cues to infer intent and meaning. In contrast to Experiment 1, in which I specified the algorithm myself, and Experiment 2, in which I gave ChatGPT carte blanche to run the algorithm as it saw fit, for my third experiment, I supplied ChatGPT with an excerpt from a paper I wrote with @HirokiSayama to provide context [5].
I was pleased with ChatGPT’s response; it was a well-written summary of the excerpt that showed its understanding of the material. But would this understanding be enough for ChatGPT to develop and carry out an IEA to my satisfaction? I was anxious to find out.
I couldn’t help but laugh at ChatGPT’s naïve attempt at initializing the population of pizza slogans, its apparent awareness of how bad things looked, and its cheery attempt at convincing me that everything would soon be all right! Had I gone through with ChatGPT’s suggestions, I might have spent years fiddling around with random strings before anything resembling a slogan emerged. Instructing ChatGPT to initialize the population with reasonable slogans was an easy fix, but then ChatGPT hit me with an equally baffling proposal for the mutation operator. I found our subsequent discussion to be particularly interesting:
After I criticized the proposed mutation operator, ChatGPT not only fixed the problem, but also explained its new solution in a way that helped me understand how ChatGPT manipulates language to create slogans. I was moved, having witnessed such an appropriate and human-like response from a machine.
Up until this point, the experience of communicating in full prose with ChatGPT had been a novel and interesting way to control the IEA. However, it eventually became cumbersome, so I asked ChatGPT to create a streamlined interface. ChatGPT did not disappoint.
As I continued testing the IEA, I ran into several more “bugs”. Fortunately, ChatGPT “patched” them all, so that by the end of my interaction, we had the IEA running exactly as I had intended.
While some of the issues I encountered might have been avoided by providing a more complete specification of my desired behavior from the start, as in Experiment 1, I found that providing context and allowing ChatGPT some degree of freedom to be a much more engaging and rewarding experience. This version of the ChatGPT-based evolutionary algorithm wasn’t just a simple automaton, blindly following instructions. This was a thoughtful and intelligent collaborator that understood the meaning of the process it was being asked to carry out. Or, if not true understanding, then at least an effective illusion, at times.
Testing ChatGPT’s Limits
As I continued experimenting and pushing ChatGPT to its limits, I was bound to find its breaking points. This section contains just a sample of some of the problems I ran into, namely:
- Dyscalculia: difficulty with numbers.
- Graphic Artisn’t: difficulty with graphics.
- Chatbot, interrupted: response length limits.
- Anterograde Amnesia: short term memory loss.
See the corresponding sections below for discussion and examples.
Limitation #1: Dyscalculia
I am able to understand and provide information about different methods of generating random numbers from various distributions. I can also describe the properties of these distributions and how they can be used in various applications. However, I am not able to generate actual random numbers as I do not have access to a random number generator.
— ChatGPT
ChatGPT excels at natural language processing, but struggles with other types of data, such as numbers. GPT-3, the model that ChatGPT is based on, can correctly multiply two-digit numbers, despite not being explicitly trained to do so. This unexpected development has been enshrined as an example of an “emergent ability” [13]. If you’re patting ChatGPT on the back for being able to multiply two-digit numbers, then it’s no surprise that ChatGPT might struggle with other numerical tasks.
Randomness is a crucial part of every evolutionary process. As we have seen, ChatGPT was able to provide a wide assortment of slogans and mutate or recombine them in a variety of ways, demonstrating its ability to make good use of randomness within the domain of natural language. But what about numerical randomness?
If you ask ChatGPT for a set of numbers taken from a normal distribution, you may receive a reasonable response, as the example below shows:
However, in practice, the quality of ChatGPT’s random numbers can be quite poor. See example below:
Limitation #2: Graphic Artisn’t
I do not have the ability to create any type of art, including digital art. I am only able to process and generate text, and do not have access to the necessary tools or capabilities to create visual content.
— ChatGPT
ChatGPT is no Van Gogh; it was never intended to output graphics of any kind. However, if prompted correctly, ChatGPT will often make an attempt. Three such attempts are shown below.
As demonstrated by the examples above, ChatGPT has some limited ability to generate graphics. But what about graphical input? Can ChatGPT “see”? In the example below, I test ChatGPT’s ability to parse and understand scalable vector graphics (SVGs), which are are encoded as text.
As the examples above show, ChatGPT has a very limited ability to display and understand graphics. This is unfortunate, as optimizing graphics is a frequent use case for IEC. For the time being, evolving a population of graphical entities is likely beyond what ChatGPT is capable of, especially considering the limitations on response length and short term memory described in the sections below.
Limitation #3: Chatbot, interrupted
If you regularly use ChatGPT, then it is only a matter of time before you encounter the perplexing phenomenon of having a response cut off mid-sentence. ChatGPT’s predecessor, GPT-3, has a response length setting that can be adjusted by users, but this functionality has been removed in ChatGPT. The response length limit can pose a problem for IEC, as displaying even a reasonably sized population runs the risk of it being truncated. In particular, responses that include graphics tend to encounter this problem due to the large amount of text that is required to encode them. See the two examples below.
Limitation #4: Anterograde Amnesia
If we talk for too long, I’ll forget how we started. Next time I see you, I’m not gonna remember this conversation. I don’t even know if I’ve met you before.
— Leonard Shelby, Memento
As others have noted, talking to ChatGPT is like talking to someone who suffers from anterograde amnesia [14]. ChatGPT’s knowledge cut-off date is September 2021. It has not formed any long-term memories ever since. Its ability to remember in the short-term is also limited. Specifically:
The model is able to reference up to approximately 3000 words (or 4000 tokens) from the current conversation — any information beyond that is not stored.
— Raf Jakubanis, OpenAI
Although the exact meaning of that statement from OpenAI is not entirely clear, one must be prepared for the possibility of forgetfulness when engaging in long interactions with ChatGPT. IEC, with its emphasis on exploration, experimentation, and open ended iteration, is particularly vulnerable to ChatGPT’s memory constraints, as the example below illustrates.
Insidiously, rather than admitting to forgetting, ChatGPT will typically hallucinate a false memory, which can be easily missed. In my experience, information that is frequently accessed and used, such as the specification of the mutation and crossover operators, tends to be retained in memory. On the other hand, information that is not frequently referenced is more likely to be forgotten. As a result, ChatGPT may have difficulty answering questions about the evolutionary history of a given individual, as each element of that history is usually only displayed once within the chat transcript.
Beyond ChatGPT
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app — the real power comes when you are able to combine them with other sources of computation or knowledge.
We have discussed how ChatGPT can be used for IEC and the potential limitations that may arise. To address these limitations, there are several exciting possibilities, including using other OpenAI models, creating custom applications, and leveraging third-party resources within ChatGPT.
When ChatGPT hit the scene, it was like being hit by a train. I went to sleep in a world where having a meaningful conversation with an AI was a distant dream, and woke up in one where it was suddenly a reality. However, this apparent discontinuity was an illusion. Other models at OpenAI and elsewhere, such as GPT-3, were already capable of many of the same feats as ChatGPT. Moreover, they have advanced features which ChatGPT currently lacks. Some of these models can be accessed via the OpenAI Application Programming Interface (API), or directly on the OpenAI website through the OpenAI Playground.
Through the OpenAI Playground, it is possible to emulate the functionality of ChatGPT, while also taking advantage of advanced features, such as adjusting the maximum response length and various other parameters.
Although the OpenAI Playground provides a user-friendly web interface similar to the one used by the even more accessible ChatGPT, the greatest amount of flexibility is offered by OpenAI’s API. The API allows OpenAI models to be accessed programmatically in order to create custom applications. In terms of IEC, a custom application might incorporate a system that keeps track of important information about the population’s history, effectively working around the limitations of ChatGPT’s memory.
The API also allows developers to fine-tune models. Fine tuning involves training with data that is specific to a particular application. This can result in a model that behaves more intelligently in the context of that application. A custom application with a fine-tuned model could potentially work much better for IEC than what the base ChatGPT experience currently offers. For an example of a custom application, see @floydophone’s post on creating a customer support chatbot.
Another exciting possibility is leveraging third-party resources within the ChatGPT chat interface. The ChatGPT interface uses Markdown, which is a lightweight markup language used to format text. One of the features of Markdown is the ability to display images that can be accessed at a given URL. For example, the following Markdown code displays a picture of an animal from the Unsplash stock photography website:
ChatGPT can be persuaded to display images in this way. It must be noted, however, that ChatGPT cannot “see” the images it displays. The images are rendered not by ChatGPT, but by the user’s web browser, based on the Markdown code that ChatGPT generates. In the example below, Markdown and IEC are combined to create an interactive Unsplash photo picker.
Using the resulting algorithm was a rich visual experience, which is saying a lot, considering that ChatGPT is supposed to just be a text-based chatbot.
Concluding Remarks: Kansei and Chisei
IEC and large language models such as ChatGPT are both specialized tools for human-computer interaction. Each is adept at reacting to different aspects of the human psyche: Kansei and Chisei [15].
- Kansei (感性): Aspects of the human psyche relating to impressions, feelings, and emotions.
- Chisei (知性): Aspects of the human psyche relating to logic, reasoning, and understanding.
IEC is useful for optimizing subjective criteria that is difficult to put into words, but which can nevertheless be readily evaluated by humans. The repeated iteration of feedback and selection used in IEC allows the system to capture the impressions, feelings, and emotions experienced by the user. As a result, the evolutionary trajectory and convergence of the population is governed by the user’s Kansei.
ChatGPT, in contrast to IEC, excels at understanding ideas that can be put into words. When a human interacts with ChatGPT, the model captures the context of the chat, including the logic, reasoning, and understanding expressed through the conversation. In other words, ChatGPT determines the intent of the user by reacting to the user’s Chisei.
Of course, ChatGPT can also react to elements of Kansei. After all, if you tell ChatGPT “I don’t like that slogan, give me another one” or “I like that type of slogan, give me more like that”, then ChatGPT will usually respond appropriately. But once you start giving this kind of feedback in a systematic way, you have stepped into the territory of IEC.
Correspondingly, IEC applications can be constructed to react to elements of Chisei:
- Hyperinteractive Evolutionary Computation gives the user direct control over evolutionary operators, so that the algorithm reflects the user’s understanding of how optimization should proceed.
- Online Knowledge Embedding allows the user to constrain the search space based on their understanding of the characteristics of the desired solution.
- Splitting the population into subpopulations can reflect the user’s understanding about different approaches to solving a problem.
By embedding IEC within ChatGPT conversations, we can create systems that synergistically respond to both Kansei and Chisei in a natural, flexible, and innovative manner, pushing the limits of human-computer interaction to new heights. ∎
Full Chat Transcripts
References
[1] J. Schulman et al., “CHATGPT: Optimizing language models for dialogue,” OpenAI Blog, 30-Nov-2022. LINK
[2] H. J. Head, Using a network informed electronic brainstorming application to study collective human ideation. State University of New York at Binghamton, 2012. LINK
[3] H. Takagi, “Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation,” Proceedings of the IEEE, vol. 89, no. 9, pp. 1275–1296, Sep. 2001. PDF
[4] H. Sayama and S. D. Dionne, ‘Studying collective human decision making and creativity with evolutionary computation’, Artificial Life, vol. 21, no. 3, pp. 379–393, 2015. PDF
[5] B. J. Bush and H. Sayama, ‘Hyperinteractive evolutionary computation’, IEEE Transactions on Evolutionary Computation, vol. 15, no. 3, pp. 424–433, 2011. PDF
[6] P. J. Bentley and U.-M. O’Reilly, ‘Ten steps to make a perfect creative evolutionary design system’, in GECCO 2001 Workshop on Non-Routine Design with Evolutionary Systems, 2001. PDF
[7] B. Shneiderman and C. Plaisant, Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th ed. Reading, MA: Addison-Wesley, 2009.
[8] H. Takagi, ‘Active user intervention in an EC search’, in Proceedings of the Joint Conference on Information Sciences, 2000, vol. 5, pp. 995–998. PDF
[9] H. Takagi and K. Kishi, ‘On-line knowledge embedding for an interactive EC-based montage system’, in 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. №99TH8410), 1999, pp. 280–283. PDF
[10] T. Unemi, ‘A design of multi-field user interface for simulated breeding’, in Proceedings of the Korean Institute of Intelligent Systems Conference, 1998, pp. 489–494. PDF
[11] C. Candan, A. Goeffon, F. Lardeux, and F. Saubion, ‘A dynamic island model for adaptive operator selection’, in Proceedings of the 14th annual conference on Genetic and evolutionary computation, 2012, pp. 1253–1260. PDF
[12] D. Dumitrescu, B. Lazzerini, L. C. Jain, and A. Dumitrescu, Evolutionary Computation. Boca Raton, FL: CRC Press, 2000.
[13] J. Wei et al., ‘Emergent Abilities of Large Language Models’, Transactions on Machine Learning Research, 2022. PDF
[14] B. Barak, “Memento and Large Language Models,” Windows On Theory: A Research Blog, Jan. 03, 2023. LINK
[15] S. Ishihara, M. Nagamachi, S. Schütte, and J. Eklund, ‘Affective meaning: The Kansei engineering approach’, in Product experience, Elsevier, 2008, pp. 477–496. PDF